TAUS Roundtable Moscow, Readability - Cornerstone of Google’s L10N Quality Evaluation System, Maxim Lobanov, Google

•

1 like•1,524 views

In modern localization world opinions really differ on readability. However for us it became the vital and crucial part of quality evaluation system. It is a catalyst that makes our QE system more objective and closer to the real life. It drives quality improvement and user satisfaction. This is a great error category that is worth to talk about.

Technology

THURSDAY,
22
May
/11:20
–
11:50

Readability:
Cornerstone
of
Google’s
L10N
Quality

EvaluaJon
System

Maxim
Lobanov,
Google

TAUS
ROUNDTABLE
2014

22
May/
Moscow
(Russia)

Maxim Lobanov
Senior Language Specialist at Google
For Czech, Hungarian, Romanian, Russian, Slovak, and Ukrainian languages
mlobanov@google.com
Readability – cornerstone of
Google l10n evaluation system

Localization Quality Evaluation (LQE)
What: Measure quality of translations provided by vendors
How: Error-typology-based review of translations
Who: Professional qualified linguists

LQE tool
– An internal tool to evaluate translation quality
– Used with Google Translation Toolkit
– All LQE results are accumulated in the LQE server

Types of errors
Projects are rated according to 6 categories of linguistic errors:
–Compliance (deviation from style guide or instructions)
–Grammar (violation of language rules)
–Meaning (translation has different meaning than source)
–Punctuation / Spelling (typos, punctuation, etc)
–Terminology (deviation from glossary/term database)
– Readability (poor flow of speech, unnatural wording, etc)
CATEGORIES BUT NOT ERRORS
–Client edits (corrections made not because of translator's fault)
–Kudos (to mark excellent translations)
Each error type has 3 severity levels with different weights

Key LQE metrics - LQE score
Errors per 1000 words
–Number of errors normalized to a document with 1000 words
–Pass rate: projects with less than 3 errors per thousand are good enough to be published

"LQE score v.s. Real life quality" mystery
Good LQE score ≠ good quality...
– With no regular errors (grammar, punctuation, spelling, terminology, etc.) translation was
still bad

Readability!
– Error category to mark unauthentic or awkward translations
– Literal translations
– Poor tone and style
– Poor knowledge of target audience
– Dull wordy texts
– No local flavor
– Lack of efforts to get context and understand products

Readability is VIP
– Really adds value to localization quality
– Helps end-users
– Helps Googlers
– Improves the writing skills of translators
– Increases attention to details
– Makes translation job meaningful
– Helps industry

And even more!
– This is one of the drivers behind Google's mission:
to organize the world's information and make it universally accessible
and useful.

Challenges
– Mentality
– Consistency
– Lack of standards
– Context
– Churn
– Underuse

What's next
– Further standardization
– More trainings and analysis
– LQE score reflecting the real life quality
– Readability and LQE applied to MT

This month marks the advent of a new generation in Machine Translation. With the release of Sovee Smart Engine 2.0, it is now possible to process virtually unlimited simultaneous transactions without the limitations originally inherent to the base Moses technology. Sovee's latest development delivers an unprecedented 500 language engines, which will expand to thousands of languages in the next few years. This workshop will demonstrate the automated language tuning and training capabilities of Sovee Smart Engine 2.0. It will highlight the deep cascading framework that delivers the highest level of accuracy ever imagined for machine translation, and a new combined process for SMT and post-editing. This presentation is a part of the MosesCore project that encourages the development and usage of open source machine translation tools, notably the Moses statistical MT toolkit. MosesCore is supported by the European Commission Grant Number 288487 under the 7th Framework Programme. For the latest updates go to http://www.statmt.org/mosescore/ or follow us on Twitter - #MosesCore

TAUS MT SHOWCASE, Moses and Other Open Resources, Maxim Khalilov, TAUS Labs, ...

TAUS - The Language Data Network

TaaS Workshop 2014, Terminology Trends- First-hand Experience as a Blogger, M...

TAUS - The Language Data Network

Mria Pia tackled the terminology trends and shared some interesting examples from her professional experience as a blogger: 1. Communicating about terminology by using social networks. 2. Social networks as available data for carrying out terminology research, in particular for monitoring language changes such as neologisms. More and more researchers are beginning to work on projects consisting in analyzing tweets to catch the next most popular word. 3. Websites are made of content and terminology is the critical part of the user experience. 4. Managing and sharing terminological data: cloud based, collaborative and social platforms. 5. The subject field of terminology is overwhelming, so some websites provide terminological resources in few clicks.

TaaS Workshop 2014, Active Terminology Prompting for SEO and Website Translat...

TAUS - The Language Data Network

This is an update regarding the EU-funded FALCON project which provides a web-based platform integrating commercial tools Easyling, XTM Cloud and TermWeb with Moses core-based SMT from Dublin City University and the use of linguistic linked data in localization workflows through a schema provided by Trinity College Dublin. The platform is compliant with Linport and other currently promoted standards.

TAUS Quality Evaluation Summit - 28 May 2015, Dublin Machine translation (MT) has been the hot-topic of the localisation industry for some time now. Buyers of localisation know they should be maximising the use of MT in their workflows, but it can be difficult to decide how, when and where to use it. Building an MT infrastructure and deploying it as part of your workflow comes at a cost – and it can be very difficult to calculate ROI. Quite often, investing in MT can come with a leap of faith. This informative presentation, presented by Tom Shaw from Capita Translation and Interpreting (Capita TI), will talk about how the TAUS DQF tools can be used to evaluate the outputs of MT from two different systems. The results of the Productivity and Quality tests can be used to help benchmark MT outputs, and can be used to help decide which engine to go with (if any), and what the associated ROI can be.

Dynamic Quality Revisited - Lena Marg (Welocalize)

TAUS - The Language Data Network

TAUS Quality Evaluation Summit - 28 May 2015, Dublin Over the past year we have been discovering that concepts once deemed “future of the translation industry” have become our day-to-day reality: translated content quality requirements have evolved, following the need to cover a wider range of translation scenarios, publish through multiple channels, do it faster and retire the content almost instantly. So what is king: "common sense" or "style guides"? Have we entered the age of "new creativity"? What does it mean to be a translator out in the field these days? We'll be discussing our findings around developing and deploying a viable "publish fast, forget fast" solution for various flavors of dynamic content, ranging from the ultimate "transcreation-level perfect" to "just actionable". The aspects we found to be of utmost importance are a very specific translator's skillset, a flexible, yet controlled evaluation environment, and, finally, the learning curve among the consumers of the translation.

TAUS MT SHOWCASE, MT & Terminology Better Together, Andrejs Vasiljevs, 10 Oct...

TAUS - The Language Data Network

Eric Blassin (Lionbridge) at the Industry Leaders Forum 2015

TAUS - The Language Data Network

2. Translation technology infrastructure The cloud seems to be the destiny of almost every application, and so it is for translation. But how do we cope with the issues. Moving to the cloud: the challenges, opportunities, pitfalls Does translation technology serve the translation industry or solve real business issues Panelists: Brendan McGuinness (Oracle), Eric Blassin (Lionbridge), Andrejs Vasiljevs (Tilde)

TAUS Post-Editing Productivity GuidelinesTAUS - The Language Data Network

Honyaku Center - TAUS Tokyo Forum 2015

TAUS - The Language Data Network

A Path to Partnership: Honyaku Center’s Adoption of Memsource Cloud). David Canek, Memsource & Hiroki Kawano, Honyaku Center Honyaku Center started experimenting with Memsource, the cloud-based CAT tool, in 2011. After making a thorough examination, it made the decision to initiate its adoption step by step. The results were good and so by the end of 2014 Honyaku Center ran the majority of its translation projects in this cloud-based CAT tool. The presenters will tell the story of building a relationship that grew into a real partnership over the course of 3 years. Honyaku Center’s Hiroki Kawano will explain the reasons behind taking the bold step of not just changing its translation technology but also adopting the cloud. He will focus on the benefits that the switch has brought about as well as the challenges that had to be overcome in the process.

Human Science - TAUS Tokyo Forum 2015

TAUS - The Language Data Network

The Potential for Utilizing Japan-developed MT Engines for Translation in Industry. Megumi Tokuda and Takeyoshi Nakayama, Human Science Currently, the use of Moses-based engines that have been developed in Europe is prevalent for machine translation within the translation industry. These engines, however, have been developed with a focus on European languages, and have been found to be lacking in quality output for languages pairs which have strikingly different linguistic structures, like Japanese and English. To address the increasing need for Japanese-English and English-Japanese machine translations, research has been progressing within Japan to create engines that can maintain high quality even between languages with unsimilar linguistic traits. Human Science, an independent organization which does not develop or sell MT engines, has been researching the potential of utilizing MT engines developed within Japan in practical translation project settings. This presentation will introduce MT engines which have been developed in Japan and have yet to be widely adopted for translation for industry. By drawing on eight years of experience in Machine Translation to present actual project data and comparisons with common MT engines, we will consider the potential of MT engines developed in Japan, not only for the Japanese-English language pair, but also between English and other Asian languages which greatly differ in linguistic structure. The presentation will cover: Examples of MT engines developed in Japan Comparisons between common MT engines and MT engines developed in Japan Potential for utilizing MT engines developed in Japan in translation for industry.

TAUS webinar The Big Picture View On The Translation Industry, March 2013

TAUS - The Language Data Network

Quality Management in Localization Certification

TAUS - The Language Data Network

Linguistic Evaluation of Support Verb Construction Translations by OpenLogos ...INESC-ID (Spoken Language Systems Laboratory - L2F)

Natural Language Processing: L01 introduction

ananth

Click here to refer to the instructions for the Unit 2 lab tasks.docx

vernettacrofts

Click here to refer to the instructions for the Unit 2 lab tasks. Discuss the importance of using a routing protocol, and explain its general functionality. Router Information Protocol (RIP) is a distance-vector routing protocol, while Open Shortest Path First (OSPF) is a link state routing protocol. Either can be used by a router to send data from one network to another. Compare and contrast distance-vector versus link state routing protocols. Explain how you would enable OSPF. Assignment Guidelines Complete the following for this assignment: Using the course materials, the textbook, and Web resources, research distance-vector routing protocols with a high concentration on the versions of RIP. Use the following questions to help you format your research paper: What is the purpose of a routing protocol and how does it differ from a routed protocol? What is a distance-vector routing protocol? What is a link state routing protocol? What are the advantages and disadvantages of distance-vector protocols? What are the advantages and disadvantages of link state protocols? What is RIP? What is OSPF? What are the two versions of RIP, and what are their specific purposes? When would you employ RIP, and when would you employ OSPF? Describe what is necessary to enable OSPF routing. Compile your responses to the above questions in a Word document of 3-5 pages. Your submitted assignment must include the following: A Word document of 3-5 pages, double-spaced, excluding the title page and reference section. All references should be in APA format. You may find that using section headings and bullets make the paper more readable. Grading You will be graded on the accuracy of your answers and the thoroughness of your RIP, RIPv1, RIPv2, and distance-vector routing protocol descriptions. Please submit your assignment. For assistance with your assignment, please use your text, Web resources, and all course materials. Grading Criteria Project Criteria Exceeds: 90%–100% Very Good: 80%–89% Meets: 70%–79% Needs Improvement: Below 70% Content (75%) Response covers all topics indicated in the assignment and adds additional content. Response covers most topics indicated in the assignment. Response covers many of the topics indicated in the assignment. Response covers none to some of the topics indicated in the assignment. Effective Communication (10%) Demonstrates outstanding or exemplary application of written, visual, or oral skills. Demonstrates outstanding expression of topic, main idea, and purpose. Audience is addressed appropriately. Language clearly and effectively communicates ideas and content relevant to the assignment. Errors in grammar, spelling, and sentence structure are minimal. Organization is clear. Format is consistently appropriate to assignment. Presentation and delivery are confident and persuasive (where applicable). The writing was of collegiate level with no er ...

Research data as an aid in teaching technical competence in subtitling

University of Warsaw

Processing short-message communications in low-resource languages

Robert Munro

NLP Tasks and Applications.ppt useful in

Kumari Naveen

lect36-tasks.ppt

HaHa501620

Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Mach...

Antonio Toral

We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three vari- ables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evalua- tors, and the provision of inter-sentential con- text. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and dis- cover that those of the experts result in higher inter-annotator agreement and better discrim- ination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.

Tech capabilities with_sa

Robert Martin

Natural Language Processing (NLP)

Abdullah al Mamun

The best known natural language processing tool is GPT-3, from OpenAI, which uses AI and statistics to predict the next word in a sentence based on the preceding words. NLP practitioners call tools like this “language models,” and they can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. Language models are already reshaping traditional text analytics, but GPT-3 was an especially pivotal language model because, at 10x larger than any previous model upon release, it was the first large language model, which enabled it to perform even more advanced tasks like programming and solving high school–level math problems. The latest version, called InstructGPT, has been fine-tuned by humans to generate responses that are much better aligned with human values and user intentions, and Google’s latest model shows further impressive breakthroughs on language and reasoning. For businesses, the three areas where GPT-3 has appeared most promising are writing, coding, and discipline-specific reasoning. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. This tool, Codex, is already powering products like Copilot for Microsoft’s subsidiary GitHub and is capable of creating a basic video game simply by typing instructions. This transformative capability was already expected to change the nature of how programmers do their jobs, but models continue to improve — the latest from Google’s DeepMind AI lab, for example, demonstrates the critical thinking and logic skills necessary to outperform most humans in programming competitions. Models like GPT-3 are considered to be foundation models — an emerging AI research area — which also work for other types of data such as images and video. Foundation models can even be trained on multiple forms of data at the same time, like OpenAI’s DALL·E 2, which is trained on language and images to generate high-resolution renderings of imaginary scenes or objects simply from text prompts. Due to their potential to transform the nature of cognitive work, economists expect that foundation models may affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.

Lepor: augmented automatic MT evaluation metricLifeng (Aaron) Han

LEPOR: an augmented machine translation evaluation metric - Thesis PPT

Lifeng (Aaron) Han

Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicateand apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances on different languages. Firstly, we design augmented factors to yield highly accurate evaluation.Secondly, we design a tunable evaluation model where weighting of factors can be optimized according to the characteristics of languages. Thirdly, in the enhanced version of our methods, we design concise linguistic feature using POS to show that our methods can yield even higher performance when using some external linguistic resources. Finally, we introduce the practical performance of our metrics in the ACL-WMT workshop shared tasks, which show that the proposed methods are robust across different languages.

Viewers also liked

TAUS MT SHOWCASE, Moses Past, Present and Future, Hieu Hoang, University of E...

TAUS - The Language Data Network

MT Benchmarking and Business Intelligence - Tom Shaw (Capita TI)

TAUS - The Language Data Network

Dynamic Quality Revisited - Lena Marg (Welocalize)

TAUS - The Language Data Network

TAUS MT SHOWCASE, MT & Terminology Better Together, Andrejs Vasiljevs, 10 Oct...

TAUS - The Language Data Network

Eric Blassin (Lionbridge) at the Industry Leaders Forum 2015

TAUS - The Language Data Network

TAUS Post-Editing Productivity GuidelinesTAUS - The Language Data Network

Honyaku Center - TAUS Tokyo Forum 2015

TAUS - The Language Data Network

Human Science - TAUS Tokyo Forum 2015

TAUS - The Language Data Network

TAUS webinar The Big Picture View On The Translation Industry, March 2013

TAUS - The Language Data Network

Quality Management in Localization Certification

TAUS - The Language Data Network

Viewers also liked (10)

TAUS MT SHOWCASE, Moses Past, Present and Future, Hieu Hoang, University of E...

MT Benchmarking and Business Intelligence - Tom Shaw (Capita TI)

Dynamic Quality Revisited - Lena Marg (Welocalize)

TAUS MT SHOWCASE, MT & Terminology Better Together, Andrejs Vasiljevs, 10 Oct...

Eric Blassin (Lionbridge) at the Industry Leaders Forum 2015

TAUS Post-Editing Productivity Guidelines

Honyaku Center - TAUS Tokyo Forum 2015

Human Science - TAUS Tokyo Forum 2015

TAUS webinar The Big Picture View On The Translation Industry, March 2013

Quality Management in Localization Certification

Similar to TAUS Roundtable Moscow, Readability - Cornerstone of Google’s L10N Quality Evaluation System, Maxim Lobanov, Google

Linguistic Evaluation of Support Verb Construction Translations by OpenLogos ...INESC-ID (Spoken Language Systems Laboratory - L2F)

Natural Language Processing: L01 introduction

ananth

Click here to refer to the instructions for the Unit 2 lab tasks.docx

vernettacrofts

Research data as an aid in teaching technical competence in subtitling

University of Warsaw

Processing short-message communications in low-resource languages

Robert Munro

NLP Tasks and Applications.ppt useful in

Kumari Naveen

lect36-tasks.ppt

HaHa501620

Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Mach...

Antonio Toral

Tech capabilities with_sa

Robert Martin

Natural Language Processing (NLP)

Abdullah al Mamun

Lepor: augmented automatic MT evaluation metricLifeng (Aaron) Han

LEPOR: an augmented machine translation evaluation metric - Thesis PPT

Lifeng (Aaron) Han

A Low Dimensionality Representation for Language Variety Identification (CICL...

Francisco Manuel Rangel Pardo

Language variety identification aims at labelling texts in a native language (e.g. Spanish, Portuguese, English) with its specific variation (e.g. Ar- gentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work we propose a low dimensionality representation (LDR) to address this task with five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain. We compare our LDR method with common state-of-the-art representations and show an increase in accuracy of ∼35%. Furthermore, we compare LDR with two reference distributed representation models. Experimental results show com- petitive performance while dramatically reducing the dimensionality — and increasing the big data suitability — to only 6 features per variety. Additionally, we analyse the behaviour of the employed machine learning algorithms and the most discriminating features. Finally, we employ an alternative dataset to test the robustness of our low dimensionality representation with another set of similar languages.

Loex2009

vanglisa vang

Cwpa 2016 comparative revision writing

macktial

Natural Language Processing

punedevscom

Basics of UX Research

The Wisdom Daily

Demystifying Natural Language Processing: A Beginner’s Guide

cyberprosocial

In today’s digital age, the realm of technology constantly pushes boundaries, paving the way for revolutionary advancements. Among these breakthroughs, one particularly fascinating field gaining momentum is Natural Language Processing (NLP). It refers to the ability of computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. This article aims to shed light on the intricacies of NLP, its applications, and its significance in various sectors.

Machine translator Introduction

Hamid Shahrivari Joghan

Going Global Without Going Insane

Kevin Potts

Similar to TAUS Roundtable Moscow, Readability - Cornerstone of Google’s L10N Quality Evaluation System, Maxim Lobanov, Google (20)

Linguistic Evaluation of Support Verb Construction Translations by OpenLogos ...

Natural Language Processing: L01 introduction

Click here to refer to the instructions for the Unit 2 lab tasks.docx

Research data as an aid in teaching technical competence in subtitling

Processing short-message communications in low-resource languages

NLP Tasks and Applications.ppt useful in

lect36-tasks.ppt

Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Mach...

Tech capabilities with_sa

Natural Language Processing (NLP)

Lepor: augmented automatic MT evaluation metric

LEPOR: an augmented machine translation evaluation metric - Thesis PPT

A Low Dimensionality Representation for Language Variety Identification (CICL...

Loex2009

Cwpa 2016 comparative revision writing

Natural Language Processing

Basics of UX Research

Demystifying Natural Language Processing: A Beginner’s Guide

Machine translator Introduction

Going Global Without Going Insane

More from TAUS - The Language Data Network

TAUS Global Content Summit Amsterdam 2019 / Beyond MT. A few premature reflec...

TAUS - The Language Data Network

TAUS Global Content Summit Amsterdam 2019 / Measure with DQF, Dace Dzeguze (T...

TAUS - The Language Data Network

TAUS Global Content Summit Amsterdam 2019 / Automatic for the People by Domin...

TAUS - The Language Data Network

TAUS Global Content Summit Amsterdam 2019 / The Quantum Leap: Human Parity, C...

TAUS - The Language Data Network

TAUS Global Content Summit Amsterdam 2019 / Growing Business by Connecting Co...

TAUS - The Language Data Network

Achieving Translation Efficiency and Accuracy for Video Content, Xiao Yuan (P...

TAUS - The Language Data Network

As contents published on the Internet are becoming more and more dominated by videos, requirements on the language translation have also changed. Specifically, video publishers and distributors have a significant interest in balancing both the translation time and the accuracy. To this end, Pactera has invested in solutions, which leverage machine translation to reduce the overall translation time, and recruit human translators to improve the accuracy in a Wikipedia-like fashion. At Pactera, we aim to help video contents to reach billions of people that were not possible before.

Introduction Innovation Contest Shenzhen by Henri Broekmate (Lionbridge)

TAUS - The Language Data Network

Game Changer for Linguistic Review: Shifting the Paradigm, Klaus Fleischmann...

TAUS - The Language Data Network

Review processes as the last step in quality assurance workflows are “notorious for causing delays and frustrations”. The reason normally is a flawed process: Many manual steps for the PMs, the lack of intuitive, layout-oriented collaboration software, plus the expectation of review to “fix a broken translation” in the last second rather than giving strategic process input. globalReview shifts this paradigm: As an integrated, collaborative platform with full layout editing it provides a positive review experience. At the same time, it pushes quality upstream applying DQF principles: Flexible content profiles define precise quality expectations; issue categories and scoring effectively gauge and also track translation quality over time; a sampling module allows for fast yet accurate quality evaluation. Put together, this allows the customer to raise the process from painful review to strategic quality management and gain valuable business intelligence.

A translation memory P2P trading platform - to make global translation memory...

TAUS - The Language Data Network

Shiyibao — The Most Efficient Translation Feedback System Ever, Guanqing Hao ...

TAUS - The Language Data Network

The presentation will introduce the NLP technologies used in Shiyibao and the main product features, covering the following points: Function of giving automatic grades for translations based on translation quality automatic evaluation algorithm; Function of giving automatic comments based on rules matching; Function of sorting translations according to their similarity or some specific fragments to dramatically improve the efficiency of reviewing and commenting on translations.

Stepes – Instant Human Translation Services for the Digital World, Carl Yao (...

TAUS - The Language Data Network

In today’s digital economy, content is becoming smaller, more fragmented, and in need of on-demand translation in minutes and around the clock. Traditional localization models are no longer sufficient in meeting these always-on, agile, fast, and small translation requirements of the digital age. This is why mobile translation services like Stepes that are able to deliver quality, speed, and scalability are poised to see tremendous growth. During this 6-minute presentation, Stepes will demonstrate live its instant human translation service for micro content. Powered by human translators from around the world, Stepes is the world’s first mobile translation ecosystem delivering quality translation services using a networking model similar to Uber and Lyft.

Farmer Lv (TrueTran)

TAUS - The Language Data Network

Smart Translation Resource Management: Semantic Matching, Kirk Zhang (Wiitran...

TAUS - The Language Data Network

The Theory and Practice of Computer Aided Translation Training System, Liu Q...

TAUS - The Language Data Network

Computer Aided Translation Training System (CATS) provides a package solutions to the problems of translation translation. CATS combines artificial intelligence, data collection, and visualization of information technology, which makes the translation teaching, class management and monitoring on one single platform areality. Translation and interpretaton teaching resources on CATS are updated regularly into detailed categories, making the teaching materials easy to access. CATS supports translation and interpretation teaching and practices, company internships as well as scientific research.

Translation Technology Showcase in Shenzhen

TAUS - The Language Data Network

How to efficiently use large-scale TMs in translation, Jing Zhang (Tmxmall)

TAUS - The Language Data Network

Most of LSPs have not converted the translated bilingual documents to TM till now. Even the LSPs have established TMs, they are also confronted with disordered management of TMs and low efficiency. This report will share the way of quick TM establishment with Tmxmall Cloud-Based Smart Aligner, the way of Management of large-scale TMs with Private Cloud-Based TM for achieving pre-translation with large-scale TMs and team cooperation and etc.. Besides, the report will introduce Tmxmall TM marketplace, which is expected to promote TM sharing. Finally, we will share the experience of LSPs on alignment and Private Cloud-Based TM management for reducing translation costs and increasing profits.

SDL Trados Studio 2017, Jocelyn He (SDL)

TAUS - The Language Data Network

SDL is the leader in global content management and language translation solutions. With more than 20 years of experience, SDL helps companies build relevant online experiences that deliver transformative business results on a global scale. Translation Industry continues to grow, and Freelancers, LSPs and Corporate clients all see increased demand as more and more content is created, so we have to address them all. As a Market-leading translation productivity tool, SDL Trados Studio is trusted by over 200,000 translation professionals to boost productivity, control quality and aid collaboration. SDL has launched Trados Studio 2017. This presentation will introduce SDL Trados Studio 2017 and highlight SDL’s new productivity booster- UPLIFT, which is well welcomed by global clients.

How we train post-editors - Yongpeng Wei (Lingosail)

TAUS - The Language Data Network

A use-case for getting MT into your company, Kerstin Berns (berns language c...

TAUS - The Language Data Network

QE integrated in XTM, by Bob Willans (XTM)

TAUS - The Language Data Network

More from TAUS - The Language Data Network (20)