9. Manuel Harranz (pangeanic) Hybrid Solutions for TranslationRIILP
This document discusses PangeaMT, a machine translation system, and experiences with hybridization. It provides a brief history of PangeaMT, describing its use of open-source Moses and capabilities. It outlines features for experts, including domain adaptation, engine creation and training. The document also discusses experiences with hybridization for linguistically distant language pairs, including challenges of word order differences and tokenization. It compares approaches using Toshiba and Mecab for Japanese reordering, finding Mecab produced higher accuracy. Future work is noted on morphology-rich languages like Russian and distant language reordering.
2. Constantin Orasan (UoW) EXPERT IntroductionRIILP
The document introduces the EXPERT ITN project, which aims to train young researchers on improving data-driven machine translation through empirical approaches. The project will support researchers during their training and research, with the goal of producing future leaders in the field. It describes the objectives to improve existing corpus-based translation tools by considering user needs, collecting data, incorporating linguistic processing, and developing hybrid approaches. The project consists of 12 individual research projects across 6 work packages and is led by an academic consortium with involvement from private sector partners.
9. Ethics - Juan Jose Arevalillo Doval (Hermes)RIILP
This document discusses ethics in the translation industry. It provides definitions of ethics from Webster's and Oxford dictionaries and lists key ethical values like integrity, transparency, and responsibility. It also outlines professional values for translators such as competence, confidentiality, and avoiding practices that undermine the profession. The document discusses issues in the industry like non-paid internships and accepting unrealistic translation projects. It provides examples of codes of conduct and outlines models for project outsourcing in the translation field.
This document provides an overview of machine translation and the Moses machine translation toolkit. It defines machine translation and statistical machine translation. It describes the major components of Moses, including GIZA++ for word alignment, SRILM for language modeling, and the Moses decoder. It explains how Moses uses phrase-based translation and tuning to produce translations. It also discusses how to set up and use a Moses server for translating webpages.
Machine translation systems can translate text from one language to another. Moses is an open-source statistical machine translation toolkit that is commonly used. It takes parallel text corpora to train models for translation. The Moses training process involves word alignment, phrase extraction, and language model building. The Moses decoder then translates new text using these statistical models.
The document discusses machine translation (MT) between Arabic and English. It covers several key topics:
1. It outlines the challenges of Arabic natural language processing and MT, including the differences between Modern Standard Arabic and dialects and a lack of annotated resources.
2. It describes different types of MT systems like direct translation engines and those using linguistic knowledge architectures. It also discusses the importance of dictionaries.
3. It discusses common MT problems such as ambiguity and differences between languages.
4. It proposes a small prototype Arabic to English MT model to demonstrate basic techniques like normalization, tokenization, stemming and using a parser and transformation rules.
9. Manuel Harranz (pangeanic) Hybrid Solutions for TranslationRIILP
This document discusses PangeaMT, a machine translation system, and experiences with hybridization. It provides a brief history of PangeaMT, describing its use of open-source Moses and capabilities. It outlines features for experts, including domain adaptation, engine creation and training. The document also discusses experiences with hybridization for linguistically distant language pairs, including challenges of word order differences and tokenization. It compares approaches using Toshiba and Mecab for Japanese reordering, finding Mecab produced higher accuracy. Future work is noted on morphology-rich languages like Russian and distant language reordering.
2. Constantin Orasan (UoW) EXPERT IntroductionRIILP
The document introduces the EXPERT ITN project, which aims to train young researchers on improving data-driven machine translation through empirical approaches. The project will support researchers during their training and research, with the goal of producing future leaders in the field. It describes the objectives to improve existing corpus-based translation tools by considering user needs, collecting data, incorporating linguistic processing, and developing hybrid approaches. The project consists of 12 individual research projects across 6 work packages and is led by an academic consortium with involvement from private sector partners.
9. Ethics - Juan Jose Arevalillo Doval (Hermes)RIILP
This document discusses ethics in the translation industry. It provides definitions of ethics from Webster's and Oxford dictionaries and lists key ethical values like integrity, transparency, and responsibility. It also outlines professional values for translators such as competence, confidentiality, and avoiding practices that undermine the profession. The document discusses issues in the industry like non-paid internships and accepting unrealistic translation projects. It provides examples of codes of conduct and outlines models for project outsourcing in the translation field.
This document provides an overview of machine translation and the Moses machine translation toolkit. It defines machine translation and statistical machine translation. It describes the major components of Moses, including GIZA++ for word alignment, SRILM for language modeling, and the Moses decoder. It explains how Moses uses phrase-based translation and tuning to produce translations. It also discusses how to set up and use a Moses server for translating webpages.
Machine translation systems can translate text from one language to another. Moses is an open-source statistical machine translation toolkit that is commonly used. It takes parallel text corpora to train models for translation. The Moses training process involves word alignment, phrase extraction, and language model building. The Moses decoder then translates new text using these statistical models.
The document discusses machine translation (MT) between Arabic and English. It covers several key topics:
1. It outlines the challenges of Arabic natural language processing and MT, including the differences between Modern Standard Arabic and dialects and a lack of annotated resources.
2. It describes different types of MT systems like direct translation engines and those using linguistic knowledge architectures. It also discusses the importance of dictionaries.
3. It discusses common MT problems such as ambiguity and differences between languages.
4. It proposes a small prototype Arabic to English MT model to demonstrate basic techniques like normalization, tokenization, stemming and using a parser and transformation rules.
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
The document provides an agenda and overview for an MT and localization quality assurance discussion. It discusses Welocalize's approach to MT, including their dedicated team of experts and experience with various MT engines. It covers analytics like automatic scoring, human evaluations, and productivity tests. It also discusses considerations for the language supply chain in terms of post-editor training and guidelines for full versus light post-editing. In summary, the document outlines Welocalize's expertise and process for designing and implementing MT programs for clients.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
This document summarizes an experiment conducted on statistical machine translation models. The experiment compared phrase-based, hierarchical, and syntax-based statistical machine translation models. The document outlines the process of data preparation including tokenization, alignment, and training on the Moses platform. It then describes how each model - phrase-based, hierarchical, and syntax-based - works, including rule extraction for the hierarchical model. The document concludes by discussing the advantage of the hierarchical model and how it was able to automatically annotate Hindi data.
An Efficient Approach to Produce Source Code by Interpreting AlgorithmIRJET Journal
This document proposes a model for converting algorithms written in natural English language into source code. It aims to help programmers by allowing them to focus on logic and problem solving without worrying about syntax. The model consists of modules for basic natural language processing, interpretation, using synonyms, and personalized training. It identifies the statement type and then parses it into formal C code by recognizing trigger words and applying rules from a case frame database. The goal is to address challenges like limited natural language understanding by making the interpreter more flexible through mechanisms like synonym recognition and personalized user training. If successful, this could help both new programmers and visually impaired developers.
cushLEPOR uses LABSE distilled knowledge to improve correlation with human tr...Lifeng (Aaron) Han
cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations. It customizes the hLEPOR metric by optimizing its parameters against LABSE similarity scores or human evaluations to achieve lower RMSE than vanilla hLEPOR or BLEU. The optimized cushLEPOR metric then shows better correlation with human judgments than existing automated metrics like BLEU.
Delivered at the European Patent Office's annual Patent Information Conference (EPOPIC 2014)
November 5th 2014
Warsaw, Poland.
In this talk, we give an introduction as to how machine translation works and what makes certain content types and languages more difficult than others.
Machine translation is the use of computers to translate text from one language to another. There are two main approaches: rule-based systems which directly convert words and grammar between languages, and statistical systems which analyze phrases and "learn" translations from large datasets. While rule-based systems can model complex translations, statistical systems are better suited for most applications today due to lower costs and greater robustness. Current popular machine translation services include Google Translate, Microsoft Translator, and IBM's statistical speech-to-speech translator Mastor.
The document discusses programming paradigms and introduces imperative programming. It defines imperative programming as a paradigm that describes computation in terms of statements that change a program's state. Imperative programming uses commands to update variables in storage and defines sequences of commands for the computer to perform. The document contrasts structured and unstructured programming and discusses concepts like l-values, r-values, iteration, and goto statements as they relate to imperative programming.
Machine translation is an easy tool for translating text from one language to another. You've probably used it. But do you know what machine translation really is? Or when you should or shouldn't use it? Navigate through this presentation to learn more!
[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
This document provides an introduction to generic programming. It discusses the motivation for generic programming, which includes providing better abstraction and reusability in programming languages. It describes two main models for generic programming - parametric polymorphism in statically typed languages and dynamic typing in dynamically typed languages. The document also discusses implementation issues around generic programming such as efficiency, code generation, and language details.
1) The document discusses a linguistic evaluation of support verb constructions performed on the OpenLogos and Google Translate machine translation systems.
2) A corpus of 100 sentences containing support verb constructions was translated into several languages by each system and evaluated both quantitatively and qualitatively.
3) The evaluation found that OpenLogos translated more support verb constructions correctly thanks to its use of linguistic rules and representations, while Google Translate struggled more with non-contiguous and idiomatic constructions due to its statistical nature.
Principles of-programming-languages-lecture-notes-Krishna Sai
This document summarizes key concepts from Chapter 1 of a programming languages textbook. It discusses reasons for studying programming language concepts, including increased ability to express ideas, improved language choice, and easier learning of new languages. It also covers programming domains like scientific, business, AI and systems programming. The document evaluates language criteria such as readability, writability and reliability. It discusses influences on language design like computer architecture and programming methodologies. It categorizes languages as imperative, functional, logic and object-oriented. Finally, it provides examples of programming environments like UNIX, JBuilder and Visual Studio.
Classification of MT-Output Using Hybrid MT-Evaluation Metrics for Post-Editi...aciijournal
Machine translation industry is working well but they have been facing problem in postediting. MT-outputs do not correct and fluent so minor or major changes need for publishing them. Postediting performs manually by linguists, which is expensive and time consuming. So we should select good translation for postediting among all translations. Various MT-evaluation metrics can be used for filter the good translations for postediting. We have shown the use of various MT-evolution metrics for selection of good translation and their comparative study
Slides for talk given at Women in Engineering on March 20, 2021.
Abstract:
Natural language understanding is a fundamental task in artificial intelligence. English understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Discovery. However, scaling existing products/services to support additional languages remain an open challenge. In this talk, we will discuss the open challenges in supporting universal natural language understanding. We will share our work in the past few years in addressing these challenges. We will also showcase how universal semantic representation of natural languages can enable cross-lingual information extraction in concrete domains (e.g. compliance) and show ongoing efforts towards seamless scaling existing NLP capabilities across languages with minimal efforts.
BERT is a language representation model that was pre-trained using two unsupervised prediction tasks: masked language modeling and next sentence prediction. It uses a multi-layer bidirectional Transformer encoder based on the original Transformer architecture. BERT achieved state-of-the-art results on a wide range of natural language processing tasks including question answering and language inference. Extensive experiments showed that both pre-training tasks, as well as a large amount of pre-training data and steps, were important for BERT to achieve its strong performance.
Lingotek provides a translation platform with computer-assisted translation tools like terminology management, machine translation, and translation memory to help reduce the amount needing translation, reuse appropriate terminology, and recycle exact matches. It offers collaborative translation features through an API and crowdsourced workflow, as well as group review and voting capabilities.
Past, Present, and Future: Machine Translation & Natural Language Processing ...John Tinsley
This was a presentation given at the European Patent Office's annual Patent Information Conference in Madrid, Spain on November 10th, 2016.
In it, we give an overview of how machine translation works, latest advances in neural MT, and how this can be applied to patents and intellectual property content, not only for translations but also information extraction and other NLP applications.
The document provides an agenda and overview for an MT and localization quality assurance discussion. It discusses Welocalize's approach to MT, including their dedicated team of experts and experience with various MT engines. It covers analytics like automatic scoring, human evaluations, and productivity tests. It also discusses considerations for the language supply chain in terms of post-editor training and guidelines for full versus light post-editing. In summary, the document outlines Welocalize's expertise and process for designing and implementing MT programs for clients.
Experiments with Different Models of Statistcial Machine Translationkhyati gupta
This document summarizes an experiment conducted on statistical machine translation models. The experiment compared phrase-based, hierarchical, and syntax-based statistical machine translation models. The document outlines the process of data preparation including tokenization, alignment, and training on the Moses platform. It then describes how each model - phrase-based, hierarchical, and syntax-based - works, including rule extraction for the hierarchical model. The document concludes by discussing the advantage of the hierarchical model and how it was able to automatically annotate Hindi data.
An Efficient Approach to Produce Source Code by Interpreting AlgorithmIRJET Journal
This document proposes a model for converting algorithms written in natural English language into source code. It aims to help programmers by allowing them to focus on logic and problem solving without worrying about syntax. The model consists of modules for basic natural language processing, interpretation, using synonyms, and personalized training. It identifies the statement type and then parses it into formal C code by recognizing trigger words and applying rules from a case frame database. The goal is to address challenges like limited natural language understanding by making the interpreter more flexible through mechanisms like synonym recognition and personalized user training. If successful, this could help both new programmers and visually impaired developers.
cushLEPOR uses LABSE distilled knowledge to improve correlation with human tr...Lifeng (Aaron) Han
cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations. It customizes the hLEPOR metric by optimizing its parameters against LABSE similarity scores or human evaluations to achieve lower RMSE than vanilla hLEPOR or BLEU. The optimized cushLEPOR metric then shows better correlation with human judgments than existing automated metrics like BLEU.
Delivered at the European Patent Office's annual Patent Information Conference (EPOPIC 2014)
November 5th 2014
Warsaw, Poland.
In this talk, we give an introduction as to how machine translation works and what makes certain content types and languages more difficult than others.
Machine translation is the use of computers to translate text from one language to another. There are two main approaches: rule-based systems which directly convert words and grammar between languages, and statistical systems which analyze phrases and "learn" translations from large datasets. While rule-based systems can model complex translations, statistical systems are better suited for most applications today due to lower costs and greater robustness. Current popular machine translation services include Google Translate, Microsoft Translator, and IBM's statistical speech-to-speech translator Mastor.
The document discusses programming paradigms and introduces imperative programming. It defines imperative programming as a paradigm that describes computation in terms of statements that change a program's state. Imperative programming uses commands to update variables in storage and defines sequences of commands for the computer to perform. The document contrasts structured and unstructured programming and discusses concepts like l-values, r-values, iteration, and goto statements as they relate to imperative programming.
Machine translation is an easy tool for translating text from one language to another. You've probably used it. But do you know what machine translation really is? Or when you should or shouldn't use it? Navigate through this presentation to learn more!
[PACLING2019] Improving Context-aware Neural Machine Translation with Target-...Hayahide Yamagishi
This is the slide used in the oral presentation at PACLING2019.
(For Japanese speakers) 本発表は私の修論発表と同等ですので、日本語がわかる方は以下のスライドの方が読みやすいかもしれません。
https://www.slideshare.net/HayahideYamagishi/ss-181147693/HayahideYamagishi/ss-181147693
Font has been changed the original one (Hiragino Maru Gothic Pro W4) into the other one by the SlideShare.
This document provides an introduction to generic programming. It discusses the motivation for generic programming, which includes providing better abstraction and reusability in programming languages. It describes two main models for generic programming - parametric polymorphism in statically typed languages and dynamic typing in dynamically typed languages. The document also discusses implementation issues around generic programming such as efficiency, code generation, and language details.
1) The document discusses a linguistic evaluation of support verb constructions performed on the OpenLogos and Google Translate machine translation systems.
2) A corpus of 100 sentences containing support verb constructions was translated into several languages by each system and evaluated both quantitatively and qualitatively.
3) The evaluation found that OpenLogos translated more support verb constructions correctly thanks to its use of linguistic rules and representations, while Google Translate struggled more with non-contiguous and idiomatic constructions due to its statistical nature.
Principles of-programming-languages-lecture-notes-Krishna Sai
This document summarizes key concepts from Chapter 1 of a programming languages textbook. It discusses reasons for studying programming language concepts, including increased ability to express ideas, improved language choice, and easier learning of new languages. It also covers programming domains like scientific, business, AI and systems programming. The document evaluates language criteria such as readability, writability and reliability. It discusses influences on language design like computer architecture and programming methodologies. It categorizes languages as imperative, functional, logic and object-oriented. Finally, it provides examples of programming environments like UNIX, JBuilder and Visual Studio.
Classification of MT-Output Using Hybrid MT-Evaluation Metrics for Post-Editi...aciijournal
Machine translation industry is working well but they have been facing problem in postediting. MT-outputs do not correct and fluent so minor or major changes need for publishing them. Postediting performs manually by linguists, which is expensive and time consuming. So we should select good translation for postediting among all translations. Various MT-evaluation metrics can be used for filter the good translations for postediting. We have shown the use of various MT-evolution metrics for selection of good translation and their comparative study
Slides for talk given at Women in Engineering on March 20, 2021.
Abstract:
Natural language understanding is a fundamental task in artificial intelligence. English understanding has reached a mature state and successfully deployed in multiple IBM AI products and services, such as Watson Natural Language Understanding and Watson Discovery. However, scaling existing products/services to support additional languages remain an open challenge. In this talk, we will discuss the open challenges in supporting universal natural language understanding. We will share our work in the past few years in addressing these challenges. We will also showcase how universal semantic representation of natural languages can enable cross-lingual information extraction in concrete domains (e.g. compliance) and show ongoing efforts towards seamless scaling existing NLP capabilities across languages with minimal efforts.
BERT is a language representation model that was pre-trained using two unsupervised prediction tasks: masked language modeling and next sentence prediction. It uses a multi-layer bidirectional Transformer encoder based on the original Transformer architecture. BERT achieved state-of-the-art results on a wide range of natural language processing tasks including question answering and language inference. Extensive experiments showed that both pre-training tasks, as well as a large amount of pre-training data and steps, were important for BERT to achieve its strong performance.
Lingotek provides a translation platform with computer-assisted translation tools like terminology management, machine translation, and translation memory to help reduce the amount needing translation, reuse appropriate terminology, and recycle exact matches. It offers collaborative translation features through an API and crowdsourced workflow, as well as group review and voting capabilities.
Webinar automotive and engineering content 16.06.16kantanmt
High quality translations that are delivered quickly are a result of a seamless and efficient translation process, but getting to this stage requires a well thought out plan, rigorous content preprocessing techniques and most importantly, clear and transparent communication between the automated translation vendor and language service provider.
In this webinar, Christian Taube and Brian Coyle discusses how the Matrix and KantanMT partnership delivers a high quality, scalable solution that increases translation productivity and supports engineering and automotive terminology standards. The webinar uses specific case study examples including a discussion on what types of content to focus on and preparing and managing Translation Memory data. Discussion includes:
• Managing content for best results
• Preparing TM data
• Tools that generate high quality results
Managing Translation Memories for Engineering and Automotive TranslationPoulomi Choudhury
High quality translations that are delivered quickly are a result of a seamless and efficient translation process, but getting to this stage requires a well thought out plan, rigorous content preprocessing techniques and most importantly, clear and transparent communication between the automated translation vendor and language service provider.
In this webinar, Christian Taube and Brian Coyle discusses how the Matrix and KantanMT partnership delivers a high quality, scalable solution that increases translation productivity and supports engineering and automotive terminology standards. The webinar uses specific case study examples including a discussion on what types of content to focus on and preparing and managing Translation Memory data. Discussion includes:
• Managing content for best results
• Preparing TM data
• Tools that generate high quality results
Good Applications of Bad Machine Translationbdonaldson
The document discusses machine translation (MT) and its potential uses and improvements. It notes that MT is showing improved productivity when integrated into workflows and that some companies are using MT to translate large portions of content. The document also discusses using MT combined with human translation and search/retrieval to provide cost-effective translation of large amounts of content.
Machine Translation Master Class at the EUATC Conference by Diego Bartolometauyou
Machine translation enables new business models to create new revenue sources for your business. However, integrating it into your workflow might be challenging. In this Master Class, Diego Bartolome will cover the most important aspects and lessons learned during the past seven years, which include technology, people, and processes.
Milengo is a privately-held joint venture of 19 leading localization companies that provides translation, localization, and language services. It has 19 local offices with over 700 translators and engineers. Milengo offers services including translation, localization, desktop publishing, software localization testing, and collaborative translation through an online platform. It works with partners like Clay Tablet and Asia Online to provide tools for quality management and machine translation.
New Breakthroughs in Machine Transation Technologykantanmt
Tony O’Dowd takes us through some of the most innovative technologies offered on the KantanMT.com platform which are helping a growing community of KantanMT users to develop and self-manage custom Machine Translation engines in the cloud.
Maxim Khalilov then illustrates bmmt’s journey with Machine Translation on KantanMT. He discusses what they have achieved so far in terms of MT engine development and showcases the value that his team is bringing to their growing international client base through the use of Machine Translation.
Welocalize Throughputs and Post-Editing Productivity Webinar Laura CasanellasWelocalize
Welocalize language tools expert Laura Casanellas details key topics related to human translation and machine translation post-editing, production, throughputs and measuring success. This is the presentation used in a recent online webinar you can find at http://www.welocalize.com/wemt/wemt-webinars/
Topics for this recorded webinar include:
- Defining throughputs for human translation and machine translation post-editing
- How to accurately compare individual throughputs for translating and post-editing
- What are the most common deviations in throughputs
- How to spot progress and performance improvement
- Who really benefits from post-editing
Learn the different approaches to machine translation and how to improve the ...SDL
SDL provides machine translation solutions to customers. They have a team of over 50 professionals across various locations that work on driving MT adoption, building custom engines, and conducting linguistic projects. SDL's approach involves evaluating data, training machine translation engines, testing outputs, and refining engines through an iterative process with a focus on maximizing quality. They provide customized solutions through domain-specific engines and language verticals to meet the needs of different customers and content types.
Björn Dieding - The Globalization Supply Chain - eZ Market TalkeZ Publish Community
The document discusses SDL Trados Translation Manager software. It describes how Translation Manager streamlines the translation process between editorial teams and translators. It integrates into content management systems like eZ Publish to optimize globalization supply chains. Translation Manager accelerates translations by up to 90% while improving quality and cutting costs by 60% by leveraging translation memories. It is most beneficial for companies with repetitive or frequently updated content that relies on professional translators and existing translation memories.
Increased automation: language service provider oneword opts for Plunet Busin...Plunet BusinessManager
oneword GmbH, a leading provider of language services, has implemented Plunet BusinessManager to further automate its translation management processes. Plunet will help oneword increase productivity by streamlining documentation and control of workflows. oneword was impressed by Plunet's expertise and aims to implement additional Plunet modules to achieve even higher levels of automation.
How to Purchase Translations and What to Look For in a SupplierResearchShare
An objective guide to managing translation services in the market research industry. Presentation by Patrick Eve, CEO of TranslateMedia. This was part of a webinar in June 2010 with GreenBook.
11. manuel leiva & juanjo arevalillo (hermes) evaluation of machine translationRIILP
The document discusses a company's evaluation of their machine translation systems. They had hoped automated metrics would correlate with productivity gains reported by post-editors, but found no correlation. Reasons for variability included different translation environments, engines, clients, post-editors, and word volumes. While some metrics indicated better translation quality, other factors like automatic terminology tools impacted productivity more. The company now combines automated metrics with time/productivity data and qualitative reviews to evaluate their machine translation performance.
Terminologist:
Supports Global Production Manager in administrative tasks Ensures consistency of terminology used across projects
Helps with communication and organization of projects Manages terminology databases
Assists with project planning and monitoring Provides terminology support to translators and project managers
Helps resolve issues and queries from project managers Researches new terminology and ensures updates to databases
Project Manager: Translator:
Acts as the main point of contact for the client Translates documents from
Savings of 83% thanks to CAT tools... [case study]Tradas
How we saved 83% of translation costs and delivery time using computer assisted translation software, translation memory and a functionality called context matching. Read this short...
Eva Klaudinyova, Localization Manager in the Globalization Program at VMware, explains the processes used in her company to control the quality of its translated materials and to measure the service level of the company's external vendors. The video of this presentation can be seen here: http://www.youtube.com/watch?v=vD5vN5MX7U8
Eva Klaudinyova, Localization Manager in the Globalization Program at VMware, explains the processes used in her company to control the quality of its translated materials and to measure the service level of the company's external vendors.
Similar to 2. Project Management - Alexandre Helle & Manuel Herranz (Pangeanic) (20)
Gabriela Gonzalez attended an expert project showcase in Rome, Italy in May 2016 where she participated in roundtable discussions on the relationship between academia, industry, and translators. She noted that while improvements are needed for translators, the main issue is whether translator needs align with industry interests. Gonzalez advocated for greater collaboration between translators, software developers, and researchers to create more user-friendly translation tools. She concluded by expressing her hope that the industry would adopt research findings and that she could be more involved in sharing experiences to improve quality assurance processes.
Pangeanic is an MT company founded in Valencia, Spain with offices in Tokyo, London, and Shanghai. Pangeanic's PangeaMT system was the first commercial application of the open-source Moses platform. It has been further developed and customized for the localization industry. Pangeanic has worked with clients such as Sony Europe to provide MT services and experiences. The company's system includes features such as monolingual training, integration with Apertium, and automated data cleaning. Pangeanic advocates for empowering translators and users in controlling MT systems and sees MT as a business opportunity to transform how translation services are provided and create new revenue streams.
Carla Parra Escartin - ER2 Hermes Traducciones RIILP
This document discusses a study on the productivity of translators when post-editing machine translation (MT) outputs compared to translating from scratch. The study was conducted with 10 in-house translators post-editing the output of an MT system customized for 3 years. It found that all but one translator were faster at post-editing MT outputs compared to translating from scratch. Automatic evaluation metrics like BLEU, TER and a fuzzy match score were found to correlate with productivity gains from MT. Thresholds for productivity gains were proposed based on these metrics.
Hermes Traducciones is the 15th largest translation company in Southern Europe and 154th globally. It is certified under quality standards ISO 9001 and EN 15038. The company has 25-30 permanent employees, over 150 freelance translators in its database, and translation teams in Portugal and Brazil. Hermes provides a wide range of translation and localization services, especially in technical fields like engineering and software. It also collaborates with universities on research projects evaluating machine translation and its potential to increase translation productivity and savings.
Lianet Sepulveda & Alexander Raginsky - ER 3a & ER 3b Pangeanic RIILP
This document describes improving hybrid translation tools using a full-text search engine approach. It discusses using natural language processing techniques and a translation memory database indexed with ElasticSearch to improve fuzzy matching. The goal is to maximize reuse of existing human translations by handling linguistic features like string transformations, part-of-speech tagging, and tokenization.
KantanMT.com is a statistical machine translation platform that is cloud-based and highly scalable. It provides automated translations at high speed and quality by fusing translation memory, machine translation, and rules. The document then discusses KantanMT's vision, some of its key features and statistics, locations it operates from including the INVENT Concept Space and School of Computing, how it obtained funding from the Commercialization Fund, and its journey from starting as a prototype to becoming widely adopted with billions of words translated.
This document describes CATaLog, a translation tool that provides:
- Incremental machine translation, automatic post-editing, and translation memory capabilities to enhance translations over time.
- Color-coded matching of source segments to translated segments to reduce cognitive load on translators.
- Online project management, translation, and review capabilities without requiring local installation.
This document discusses optimizing machine translation systems for user benefit. It outlines several ways to measure translation quality and utility, including editing time and effort. Current approaches include post-processing machine translation, learning from translator feedback, and using quality estimation to guide humans. The document advocates formalizing the task purpose and taking advantage of user context to explicitly train systems to maximize user benefit, such as optimizing interactive prediction for translation or post-editing tasks. The vision is for task-based optimization to be applied beyond machine translation to any user-agent interaction scenario.
The document summarizes the results of a survey investigating the needs and preferences of translators regarding translation technologies. The survey looked at translators' usage of computer-assisted translation (CAT) tools, machine translation, terminology management tools, and corpora. It found that while CAT tools are widely used, features like machine translation and terminology management that appear as both most useful and most disliked require further improvements to be truly useful. Respondents emphasized needing tools that are simple to use and integrate multiple resources like translation memories and corpora. The survey revealed both opportunities to better meet translators' needs and their varying attitudes towards the role of technology in translation work.
This document discusses quality estimation of machine translation using the QuEst++ framework. It summarizes that QuEst++ can predict the quality of unseen machine translated text using only the source and target texts without references, extracting features to build models that estimate metrics like post-editing effort and time from limited labeled training data. The framework extracts features at the word, sentence and document level from the source and target texts and information from the machine translation system, then trains models using those features to predict quality scores for new translations.
The document discusses evaluating terminology tools through their features. It first introduces how terminology is important for translation and natural language processing. It then explores the features of Terminology Extraction Tools and Terminology Management Tools. These include functions like term extraction, context extraction, and glossary management. The document evaluates several specific tools to compare their feature sets. It concludes by emphasizing the importance of identifying user needs and systematically testing tools to select the most appropriate one.
This document discusses combining translation memory (TM) and statistical machine translation (SMT). It summarizes that TM works best for repetitive text but SMT is more reliable when there are no close matches. It then reviews the speaker's previous work on combining TM and SMT during decoding and before decoding, and presents results showing BLEU score improvements on several language pairs.
The document discusses the differences between how ontologies are used in scientific research versus industry. In scientific research, ontologies focus on creating and extending existing generic ontologies and validating ontology induction methods, using ontologies to improve natural language processing technologies. In industry, ontologies are used as value-adding knowledge bases for various purposes like matching product reviews to categories, terminology standardization in machine translation, and matching resumes to jobs. The document argues that bridging the gap between scientific and industry usage of ontologies requires more domain-specific data and discoveries, true application focus, and open data flow.
This document discusses Acclaro's quality management program. It introduces their services and clients, then describes their quality program which uses a customized memoQ feature to track errors. It discusses two client cases, including a technical software company and an online media company. The quality assurance model and customization options are demonstrated. Benefits include quantitative measurement and issue identification. Challenges include scalability and technical bugs. The goal is more integrated quality reporting and useful statistics.
ER1 Eduard Barbu - EXPERT Summer School - Malaga 2015RIILP
The document discusses collecting and cleaning multilingual data. It describes estimating the amount of parallel data that exists in Common Crawl, testing different crawlers, and developing a machine learning approach to classify translation units as either true translations or errors. Key points include estimating that Common Crawl contains around 1 billion parallel pages, crawlers tested had low recall, and the best performing model for classifying translation units was an SVM classifier with an F1-score of 0.81.
ESR1 Anna Zaretskaya - EXPERT Summer School - Malaga 2015RIILP
This document summarizes the results of a survey on machine translation (MT) usage among professional translators. Some key findings include:
- 36% of respondents currently use MT, while 38% do not use it and do not plan to. Most saw potential benefits from high-quality MT.
- MT is used equally for resource-rich and resource-poor languages. Technical domains like ICT saw higher MT usage.
- Higher computer competence and IT training were associated with greater MT use. Translators working with agencies also used MT more.
- While MT can provide benefits, respondents noted it cannot replace humans and may threaten jobs or lower wages. Better quality is needed.
ESR2 Santanu Pal - EXPERT Summer School - Malaga 2015RIILP
The document describes a statistical automatic post-editing (APE) system that aims to improve machine translation output with minimal human effort. The system uses hierarchical phrase-based statistical machine translation trained on machine translation output and reference human translations. The system first cleans and preprocesses data, generates improved word alignments, and then performs hierarchical phrase-based SMT to output post-edits. Evaluation shows the APE system outperforms the baseline machine translation according to both automatic metrics and human evaluation, requiring less post-editing effort.
ESR3 Hernani Costa - EXPERT Summer School - Malaga 2015RIILP
This document summarizes a study that investigates using distributional similarity measures (DSMs) to assess the relatedness between documents in comparable corpora. The study uses three DSMs - number of common entities, Spearman's rank correlation coefficient, and Chi-square - on four subcorpora from the INTELITERM corpus. The results show the subcorpora generally contain highly related documents, though the smaller Spanish translated corpus shows more inconsistency. Future work could involve expanding experiments to other languages and DSMs, and using the approach to filter unrelated documents.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
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Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
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ملزمة تشريح الجهاز الهيكلي (نظري 3)
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2. Project Management - Alexandre Helle & Manuel Herranz (Pangeanic)
1. Introduction
Translation project tools
Professional translation workow
Project Management
From industrial perspective
A. Helle M. Herranz
Pangeanic - BI-Europe
EXPERT Summer School, 2014
A. Helle, M. Herranz Project Management
2. Introduction
Translation project tools
Professional translation workow
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
3. Introduction
Translation project tools
Professional translation workow
Project Management System
Translation Companies need a Project Management System,
just like any other company
European Standard EN15038 establishes a quality system
Came into force 1 /08/ 2006 replacing 30 previous national
standards
It certies the process and the service as well as other aspects
like QA / QC and traceability
It establishes procedures for
Human Resources Management
Technical Resources Management
Managing Relations with Clients
Vendor Management
Process Management (Service, ie the translation procedure)
Process Verication, check, QC/QA
Customer Satisfaction (metrics)
Non-Conformities, Auditing, etc
A. Helle, M. Herranz Project Management
4. Introduction
Translation project tools
Professional translation workow
Typical interaction at a translation company
A. Helle, M. Herranz Project Management
5. Introduction
Translation project tools
Professional translation workow
Typical order workow at a translation company
A. Helle, M. Herranz Project Management
6. Introduction
Translation project tools
Professional translation workow
Typical translation job project management at a translation
company
A. Helle, M. Herranz Project Management
7. Introduction
Translation project tools
Professional translation workow
Cost of translation
A. Helle, M. Herranz Project Management
8. Introduction
Translation project tools
Professional translation workow
Massive amounts of data - Is language business manageable?
A. Helle, M. Herranz Project Management
9. Introduction
Translation project tools
Professional translation workow
TM and TMX
Translation Memories (TM)
database that stores segments that have previously been
translated, in order to aid human translators
used by:
Computer Assisted Translations tools (CAT tools)
Word processing programs
Terminology management systems
Multilingual dictionaries
Machine Translation systems
Many companies producing multilingual documentation are
using translation memory systems
In a survey of language professionals in 2006, 82.5% out of
874 replies conrmed the use of a TM
TMX
XML specication for the exchange of TM data between CAT
and localization tools with little or no loss of critical data
A. Helle, M. Herranz Project Management
10. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
12. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
13. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software
There are several software programs designed to aid all the
above and above all, ease admin tasks, traceability and quality
metrics
XTRF (Poland)
Across (Germany)
Plunet (Germany)
A. Helle, M. Herranz Project Management
14. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software (2)
A. Helle, M. Herranz Project Management
15. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software - Internal sta
A. Helle, M. Herranz Project Management
16. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software - External sta
A. Helle, M. Herranz Project Management
17. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software - Customers
A. Helle, M. Herranz Project Management
18. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software - Quote
A. Helle, M. Herranz Project Management
19. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Project management software - Project
A. Helle, M. Herranz Project Management
20. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
21. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Memory-based systems (CAT tools)
Main tool of the language professionals
Use of TMs
Shows us similar (hight percent match) translations stored in
the TM for each segment of translation
100% matches: identical segments in the TM
Fuzzy matches: there are no 100% matches, but there are
similar segments. Generally, matches below 70% are not useful
Mean productivity: 2000-3000 words/day
E.g.:
Private software: SDL Trados, MemoQ, MemSource
Opensource software: OmegaT
A. Helle, M. Herranz Project Management
22. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Memory-based systems (CAT tools) (2)
Advantages:
More productivity in professional translation
Very rened and extended
Friendly GUI
Eases the Quality Check (QC)
Possible to integrate MT
Disadvantages:
Translation not adaptable
If there isn't any TM available, there is few productivity
improvement
A. Helle, M. Herranz Project Management
23. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Machine Translation systems (MT)
Statistic-based systems
Rule-based systems
Hybrid systems
Advantages:
Translation adaptable
More productivity in non-professional translations
Disadvantages:
Needed a lot of data to create a engine
MT itself isn't useful for professional translation, only for
gisting
A. Helle, M. Herranz Project Management
24. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
CAT tools + MT
First done in the 90s between Trados and Systran
With this approximation, the CAT tool shows similar
translations from TM and translation from MT, and the user
chooses what want to postedit. E.g.:
translate segments with matches above 75% with TM
translate segments with matches below 75% with MT
Very used approximation
but there are professional translators reluctant to use MT
postedit is faster than translate from zero
it makes possible to sell cheaper translations
Mean productivity: 5000 words/day
A. Helle, M. Herranz Project Management
25. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
CAT tools + MT (2)
Advantages:
Future of the translation
Enable faster translations
Fast postedition if the MT output is good
Disadvantages:
If the MT output is bad, it's faster to translate from scratch
A. Helle, M. Herranz Project Management
26. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
CAT tools + MT (3)
A. Helle, M. Herranz Project Management
27. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
CAT tools + MT (4)
Video: Pangeanic's machine translation demo in SDL Studio -
German
A. Helle, M. Herranz Project Management
28. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
29. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Quality Check software
Software to ensure quality of the translation
Misspellings
Tag mismatches
Numeric mismatches
Terminology inconsistencies
...
E.g.: Xbench, QA Distiller
A. Helle, M. Herranz Project Management
30. Introduction
Translation project tools
Professional translation workow
Translation project tools
Project management software
Translation software
Quality Check software
Quality Check software (2)
A. Helle, M. Herranz Project Management
31. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
32. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Translation project management without MT
A. Helle, M. Herranz Project Management
33. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Outline
1 Introduction
2 Translation project tools
Translation project tools
Project management software
Translation software
Quality Check software
3 Professional translation workow
Translation project management without MT
Translation project management with MT
A. Helle, M. Herranz Project Management
34. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Translation project management with MT
A. Helle, M. Herranz Project Management
35. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Translate the original le
A. Helle, M. Herranz Project Management
36. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Translate the original le (2)
Translate the original le
Advantages:
Directly
Disadvantages:
Dicult postedition (can't use CAT tools)
Dicult QC (not possible with XBench or QA Distiller)
A. Helle, M. Herranz Project Management
37. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Translate the bilingual le
A. Helle, M. Herranz Project Management
38. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Translate the bilingual le
Translate the bilingual le
Advantages:
Fast if we don't have TM
Disadvantages:
Slow if we have TM
A. Helle, M. Herranz Project Management
39. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Oine MT translation
A. Helle, M. Herranz Project Management
40. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Oine MT translation(2)
Oine translation
How it works:
During pre-analysis, export unknown segment (segments
under XX percent match) into bilingual le
Translate bilingual le with MT
Import MT translated bilingual le into the TM
Select a penalty for MT
We can choose TM or MT
Advantages:
Fast postedition
Oine
Disadvantages:
A few steps more to do preparing the project
A. Helle, M. Herranz Project Management
41. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Online MT translation
A. Helle, M. Herranz Project Management
42. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
Online MT translation(2)
Oine translation
How it works:
Install and congure a plugin for MT in the CAT tool that
enables online translation
Select a penalty for MT
We can choose TM or MT
Advantages:
Fast postedition
A few steps less to do preparing the project
Disadvantages:
Latency depends of Internet
A. Helle, M. Herranz Project Management
43. Introduction
Translation project tools
Professional translation workow
Translation project management without MT
Translation project management with MT
END
Questions?
A. Helle, M. Herranz Project Management