Just as defining workflows and putting in place metrics are essential parts of an effective corporate strategy for post-editing machine translation (PEMT), so is the development of a post-editing guide for linguists. Z-Axis Director Uwe Muegge will present a simple approach to writing a PEMT guide. He will demonstrate how a PEMT guide has a positive impact on post-editing output, which in turn frees up linguistic QA resources for more important work.
This document provides guidelines for post-editing machine translated text. It defines two levels of expected quality: "good enough" quality and quality similar to human translation. For "good enough" quality, the focus is on semantic correctness while stylistic edits are not required. For quality similar to human translation, the text should be grammatically correct, with accurate terminology and formatting. The guidelines are meant to help set expectations between customers and service providers for post-editing work.
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.
The document provides information about translation skills for a course on translation. It discusses teaching translation, including knowing student backgrounds and explaining the translation process. It also covers qualities of good translations, translation tools, working with clients, and translator ethics. Machine translation is noted as sometimes useful for a rough draft but requires human editing.
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!
The document discusses various technical components of the translation process. It describes translation as involving interpreting the source text, applying skills to render the meaning in the target language, and re-expressing that meaning. The document outlines different options for translation, including direct/literal translation and oblique translation. It also distinguishes between factual knowledge of languages and procedural knowledge of translation techniques.
This document provides guidelines for post-editing machine translated text. It defines two levels of expected quality: "good enough" quality and quality similar to human translation. For "good enough" quality, the focus is on semantic correctness while stylistic edits are not required. For quality similar to human translation, the text should be grammatically correct, with accurate terminology and formatting. The guidelines are meant to help set expectations between customers and service providers for post-editing work.
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.
The document provides information about translation skills for a course on translation. It discusses teaching translation, including knowing student backgrounds and explaining the translation process. It also covers qualities of good translations, translation tools, working with clients, and translator ethics. Machine translation is noted as sometimes useful for a rough draft but requires human editing.
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!
The document discusses various technical components of the translation process. It describes translation as involving interpreting the source text, applying skills to render the meaning in the target language, and re-expressing that meaning. The document outlines different options for translation, including direct/literal translation and oblique translation. It also distinguishes between factual knowledge of languages and procedural knowledge of translation techniques.
[TL09] 突撃! 隣の Visual Studio Team Services / Team Foundation Server ~利用者からのベスト...de:code 2017
Visual Studio Team Services / Team Foundation Server を活用することで開発効率を改善することができますが、いろいろな機能があるためどう活用すべきか悩まれている方も多いのではないでしょうか。
本セッションでは、Visual Studio Team Services/Team Foundation Server のベストプラクティスを利用者の声とともにお届けします。
製品/テクノロジ: Microsoft Azure/TFS/VSTS
武田 正樹
日本マイクロソフト株式会社
開発ツール推進部
テクノロジー スペシャリスト
額賀 義則
株式会社 日立産業制御ソリューションズ
生産統括本部 業務改革本部 生産技術部
技師
井上 宗治
株式会社ジュピターテレコム
情報システム本部 システム企画部
The document introduces the Dynamic Quality Framework (DQF), which aims to standardize quality measurements across the translation industry. It describes DQF as inclusive, industry-shared, and data-informed. The framework integrates with common CAT tools and TMS through open APIs to collect translation and review data and provide interactive dashboards and reports for performance tracking and benchmarking at the project and organizational level.
More Related Content
Similar to How to write a Post-Editing Guide that will optimize your QA Process, by Uwe Muegge, Z-Axis Tech Solutions
[TL09] 突撃! 隣の Visual Studio Team Services / Team Foundation Server ~利用者からのベスト...de:code 2017
Visual Studio Team Services / Team Foundation Server を活用することで開発効率を改善することができますが、いろいろな機能があるためどう活用すべきか悩まれている方も多いのではないでしょうか。
本セッションでは、Visual Studio Team Services/Team Foundation Server のベストプラクティスを利用者の声とともにお届けします。
製品/テクノロジ: Microsoft Azure/TFS/VSTS
武田 正樹
日本マイクロソフト株式会社
開発ツール推進部
テクノロジー スペシャリスト
額賀 義則
株式会社 日立産業制御ソリューションズ
生産統括本部 業務改革本部 生産技術部
技師
井上 宗治
株式会社ジュピターテレコム
情報システム本部 システム企画部
The document introduces the Dynamic Quality Framework (DQF), which aims to standardize quality measurements across the translation industry. It describes DQF as inclusive, industry-shared, and data-informed. The framework integrates with common CAT tools and TMS through open APIs to collect translation and review data and provide interactive dashboards and reports for performance tracking and benchmarking at the project and organizational level.
The document discusses the evolution of machine translation (MT) technology over time from early conceptual ideas to modern neural machine translation (NMT) systems. It uses metaphors of a band changing their sound over time by adding new band members, such as an "MT guy", to represent how translation companies can adapt to new technologies. The presentation encourages translation businesses to thoughtfully integrate new tools like NMT by involving stakeholders and focusing on people in the process of transition.
The document summarizes the results of a machine translation evaluation that compared human and machine translations. Several human and machine translation systems were evaluated on a test set containing sentences translated between English and Chinese. The top performing systems were combinations of human and machine translations. There was criticism of claims of machine translation achieving "human parity" due to limitations in the test set only using sentences rather than documents, and evaluators not being qualified translators. Neural machine translation systems are argued to have advantages over statistical and rule-based systems by processing full sentences and storing additional context in hidden layers.
The document discusses how artificial intelligence and neural machine translation will change the role of human translation over time. While AI can handle the translation process at scale, humans will still be needed for local knowledge, problem solving, and tasks like optimizing processes, improving output quality, and ensuring quality. However, a fragmented technology landscape slows businesses down. The solution proposed is an integrated localization hub that connects content systems, translation technology, and translation services through a single API to address current issues where technical knowledge and system fragmentation are still barriers.
The document discusses innovation in machine translation and language technology. It notes that translation is becoming more data-driven and algorithmic, with machines learning from large amounts of data. It also mentions that translation may become invisible and automated like utilities such as electricity. The document then lists some concepts characterizing innovative contest candidates in game changer awards, such as advanced machine translation, artificial intelligence, and automated quality evaluation. Finally, it states that six contestants will each have six minutes to pitch their innovative ideas.
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 global P2P Trading Platform for TMs will be introduced. Tmxmall TM marketplace is the core, and client TM software and CATs are the input and output respectively. User of CATs is able to search the TMs of client users while it does not require client users to upload TMs to the cloud.
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.
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.
This document discusses TruTran's open machine translation platform and the trends in machine translation engine development. It notes that neural network technology allows each company to have its own customized trained neural machine translation engine. The open source nature of neural networks means that machine translation will be "generalized" or available to more users. However, enterprises currently lack professionals skilled in natural language processing and training data can be difficult to process. TruTran's platform aims to address these issues by allowing users to easily upload custom training data and corpora, select a domain to train an engine, and have the engine trained within 6 days on the platform's resources. This gives each company their own commercial-grade machine translation engine at low cost and with their
Kirk Zhang, the COO of Wiitrans, presented on their semantic matching and translator resource management tools which aim to deliver high quality translations by matching content to appropriate translators based on their individual translator profiles and histories. The tools analyze translator-specific language assets, glossaries, and translation memories to best match work to translators and simplify the translation process.
The document describes a computer-aided translation and interpretation training system called CATS. It provides course management, multi-lingual resource centers, and translation management platforms to support online translation and interpretation courses. CATS allows instructors to upload multimedia content and documents, create translation cases and assignments, and evaluate student work. It aims to improve over traditional methods of collecting assignments through email by offering an integrated online platform for pre-class, in-class, and post-class activities.
The document announces a Translation Technology Showcase event hosted by TAUS on February 28, 2017 in Shenzhen. The event will feature presentations from various translation technology companies on topics like multichannel translation for the digital economy, using free and open source tools, leveraging large translation memories, and neural machine translation. The agenda lists out the scheduled presentations and their times. The document also mentions that TAUS recently published an updated Translation Technology Landscape Report covering trends in the industry and profiles of over 80 companies.
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 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.
This document discusses Lingosail's translation technology products and services, including machine translation, corpus construction, and translation services. It outlines how Lingosail's machine translation process editing (MTPE) solutions can provide easier entry into translation for clients, higher translation efficiency, and more scalable management of translation workflows. The document also describes Lingosail's patent post-editing training course for translators, which saw hundreds of participants last year, and resulted in trainees increasing their translation speed and quality after training.
This document discusses how to introduce machine translation (MT) into a company to improve localization processes. It outlines challenges with the current process of 30 localization loops involving 40 translators across different locations with no quality or cost control. Introducing MT for display text localization could speed up availability, lower costs by 25%, and reduce unnecessary translation loops by 50%. A short-term goal is to use MT for development phases with a final quality loop involving human translation and post-editing. Long-term preparation is needed to expand MT use while addressing risks, quality guidelines, and system environments.
This document discusses integrating XTM Cloud and TAUS DQF to enable higher quality translation projects. Key steps include creating accounts in both systems, configuring LQA parameters and issues in XTM, creating translation projects in XTM with LQA steps, performing translations and LQA reviews in XTM, and then viewing productivity and quality results in the TAUS DQF system. The integration is meant to provide benefits like higher productivity, improved quality, and better data to evaluate machine translation systems.
Quality standards in the industry have come a long way. They have evolved over the years, but their focus on quality definitions based on errors and metrics has remained the accepted wisdom. Expectations of end users are changing. Every piece of content has a job to do, and it is often to touch the heart of users rather than just the mind by delivering information that is accurate and whose quality is measurable. A new “quality evaluation paradigm” is emerging. This calls for a new profile for translators, one that is different from what has been typical for the past few decades. This presentation will look at this trend in more detail, considering how to test these new types of translators fast and effectively. What matters in this emerging quality model and what does it possibly mean for DQF?
3. TAUS MT Post-Editing Guidelines
• Four guides in one document
• Evaluating Post-editor Performance
• MT Post-editing
• Post-editing Productivity
• Pricing Machine Translation Post-
editing Guidelines
• Post-editing guide
• Defines quality levels
• Provides concrete recommendations
• Available for free from TAUS
website
Home>Academy>Best Practices>Post-
Edit>MT Post-Editing Guidelines
4. ISO 18587 Post-editing of machine translation
• Main part includes sections on
• Post-editing process
• Qualifications of post-editors
• Requirements of full post-editing
• Annex includes sections on
• Post-editor training
• Light post-editing
• Pre-editing
• Available for CHF58 from ISO
website
www.iso.org>Search ISO>18587
6. Require the use of a translation memory system
• Translation memory systems are a
superior editing environment
• Source and target segments are in
close proximity and in synch
• Simple completeness check (it’s hard
to miss a segment!)
• Automatic terminology recognition
• Automatic fuzzy matching and
manual concordance search ensure
consistency
• Many systems feature automatic
quality assurance functions
7. Require that terminology be managed
• In some post-editing projects, clients
may tolerate grammatical and stylistic
imperfections. Incorrect terminology is
almost always an issue.
• Having project-specific terminology
available in a translation memory
system dramatically improves post-
editing quality and productivity.
• Especially in large projects involving
multiple post-editors, managing
terminology can make the difference
between success and failure.
9. Identify issues by measuring post-edit distance
Use a tool for measuring post-edit distance (e.g. TAUS DQF, Post-Edit Compare,
Welocalize weScore) to identify major recurring problems.
10. Use your translation memory system to identify major recurring problems.
Note: This method is very labor-intensive and time-consuming!
Identify issues by inspecting translation memories
12. Start with one post-editing guide
per language.
If your post-editing projects vary
greatly, write separate guides
rather than including too many
rules in one guide.
Keep your
post-editing
guide simple
and short
Focus on the
biggest
issues
14. Stakeholder Alignment Maturity Plan
Level 1
INTRODUCTION
(Client stakeholder signs off on all assets)
Level 2
CLIENT STAKEHOLDER ALIGNMENT BUILDING
(Client stakeholder signs off on high visibility assets
only)
Level 3
CLIENT STAKEHOLDER ALIGNMENT MAINTAINANCE
(QA signs off on all assets)
Meet & Greet between Client Stakeholder & QA
Mgmt
1) Explain QA Mgmt role & 2) Set QA Mgmt as POC for
quality
Note: QA Mgmt should proactively look for the best
ways to offload Client Stakeholders from linguistic
review and maintain high quality of the translations at
the same time.
Proactive approach by QA Mgmt
Driving communication around resolving quality
issues, reaching out to Client Stakeholder delaying
sign off, supporting Client Stakeholder-QA
alignment/communication
Maintaining alignment with Client Stakeholder
Periodical meetings with Client Stakeholder, survey,
to check a satisfaction level from QA services
Maturity Levels Level Description Estimated Timeline
GROUP-1 LANG.
Avg. volume/quarter
> 300k words
FIGS, RU, PL, TR, NL, CZ, JA,
KO, CN, ES-LA, FR-CA, PT-BR
Starting point
After 3 months
After 6 months
GROUP-2 LANG.
Avg. volume/quarter
< 300k words
AR, SK, RO, FI, SE, NO, DA,
VI, TH, IN, UA, CRO, SL, BUL
Starting point
After 6 months
After 9 months