"There is no choice but to use it."

Machine Translation acceptance among the Language Industry

(JTFMT)

MT Summit XVI JTF Workshop

Machine Translation acceptance among the Language Industry

Thank you for your participation.

This workshop ended successfully. Thanks to everyone who came, we would like to thank panelists and the staff who worked for this workshop and all the people concerned. Thank you for your cooperation. - JTF

What you can find in this workshop:

1. How does the translation industry accept (or resist) MT in Europe and the U.S. where the acceptance of MT has already occurred several years ahead of Japan? We will ask the panelists from Europe/US to share their experience and prospects of the near future Japan will have (maybe in the next three years) in the first part of this program.

2. In Japan, recognition of the fact that acceptance of MT in the translation industry is an urgent issue, rapidly spreading after the appearance of Google NMT. Panelists from Japan will report the latest MT acceptance situation in Japan in the second part of this program.

3. In the last part of this program, Discussion will be given by all panelists on the outlook for MT acceptance in the future Japanese translation industry and what Japan should learn from the European/US experience to advance as a more constructive step.

Timeline:

13:00-13:50 "Recipe for High Quality Machine Translation", Masao UCHIYAMA (NICT)

14:00-14:10 Opening

14:10-14:50 Invited Talk "From Post-editing to Hybrid-intelligence translation: How to create a real partnership between humans and machines", Mike Dillinger, PhD (LinkedIn)

14:50-15:10 "Cats, Dogs and how online training will change neural MT", Manuel Herranz (CEO, Pangeanic)

15:10-15:30 "Customising Neural Machine Translation Engines for a Productive Translation Industry", Dimitar Shterionov, PhD (KantanMT)

15:30-15:40 10 minutes Break

15:40-16:00 "Three factors for raising productivity of industrial translation by machine translation", Hiroki KAWANO (PostEditTokyo)

16:00-16:20 "How Translation Industry Should Adopt Machine Translation", Gen SATO (SDL Japan)

16:20-16:30 10 minutes Break

16:30-17:30 Panel Discussion

Recipe for High Quality Machine Translation

Masao Utiyama, PhD, NICT

Masao Utiyama is a research manager of the National Institute of Information and Communications Technology, Japan. His main research field is machine translation. He leads the development of "Minna No Jido Hon'yaku @ TexTra"

[Invited Talk] From Post-editing to Hybrid-intelligence translation:

How to create a real partnership between humans and machines

Mike Dillinger, PhD, LinkedIn

Mike Dillinger, PhD, is manager of taxonomies and MT at LinkedIn. Before that, he was manager and computational linguist at eBay, an independent consultant for Fortune 500 companies, and director of linguistics at both Spoken Translation and Global Words. He has two MT-related patents.

MTSummitXVI_JTFWorkshop_1_MikeDilinger.pdf

Abstract

We often hear that computers are going to eliminate the need for human translators. Yet paradoxically here we are: How many translation companies have started mass layoffs? How many translators are driving taxis for lack of work? None. In fact, the translation industry is growing: there is more translation work than ever. We need human translators more than ever, even with a huge increase in the use of MT.

Behind the hype in the press, the anxiety of the translator community, and most approaches to MT research lies the simplistic and very questionable assumption that we have to decide "Will humans or machines do translation?". But that's not even the right question to ask. In this talk I'll explain why.

We know from a wide range of other areas that hybrid systems with humans and machines working together are far more effective than humans or machines alone. Large companies and large translation service providers face problems that simply cannot be solved without more and better technology. So we have to find out how we can create a more effective partnership between human translators and machines to solve them. In this talk, I'll describe some of the specific problems we need to address and progress that is happening now.

Cats, Dogs and how online training will change neural MT

Manuel Herranz, CEO, Pangeanic

Manuel Herranz, CEO of Pangeanic, established Pangeanic in 2005. Collaboration with Valencia’s Polytechnic research group and the Computer Science Institute led to the creation of the PangeaMT platform, becoming the first LSP in the world to implement open source Moses successfully in a commercial environment.

(you can read a longer bio here)

MTSummitXVI_JTFWorkshop_2_ManuelHerranz.pdf

Abstract

Neural networks need samples to learn the patterns that make an MT system, a picture catalogue system or even a summarization and chat system. Amazingly, the volume of data is typically less than the data required for a statistical machine translation system since it is the network that works out the relations and structure, thus obtaining more "fluency".

Neural networks produce human-like quality in practically all language pairs, and the outperform SMT in distant language pairs for Japanese/Arabic/Russian/Turkish <-> English as recent studies demonstrate.

However, there are known problems with NMT, such as terminology control, handling tags and fast re-training, which were solved in SMT a long time ago.

My presentation will deal with how NMT is likely to progress to incorporate online learning and achieve even higher levels of customization, such as the work Pangeanic will conduct for the EU in the iADAATPA project.

Customising Neural Machine Translation Engines for a Productive Translation Industry

Dimitar Shterionov, PhD, KantanMT

Dimitar Shterionov, head MT researcher at KantanMT, leads KantanLabs, a research group committed to advancing language technology. Shterionov holds a PhD in computer science from KU Leuven Belgium. He has worked on design and development of artificial intelligence software for learning and reasoning with uncertain data.

(you can read a longer bio here)

MTSummitXVI_JTFWorkshop_3_DimitarShterionov.pdf

Abstract

With increasing translation demands due to rapid globalisation, Machine Translation (MT) has become a de-facto tool for a productive translation industry. With the emergence of Neural Machine Translation (NMT) in mid 2016, came promises for even better MT quality and for increased productivity. However, getting the best out of NMT requires a lot of work. One of the main challenges faced by those working with NMT engines is controllability.

Efficiently tackling this challenge is a key to successfully integrating NMT in a real-life translation production line. In this talk three ways to control the translation flow of NMT engines will be presented: data pre- and post- processing mechanisms; incremental training and engine adaptation; and enforcement of non-translatable assets.

This presentation will look at the effect of these procedures on the NMT output, their pros and cons, and will also discuss use-cases to show how an NMT engine can be customised towards specific translation requirements. All these controls will be discussed within the scope of an industry established MT platform – KantanMT.

Presenting these control mechanisms aims to show the necessary human-machine symbiosis that leads to an efficient NMT engine and therefore, to a successful productive translation workflow.

Three factors for raising productivity of industrial translation by machine translation

Hiroki Kawano, PostEdit Tokyo

After several years working in the space development field switched his focus to localization and translation, running his own company from 1991. His company PostEdit.Tokyo provides consulting to translation firms looking to introduce MT. Since 2011 has served as the editor of the JTF Journal, the monthly magazine of the Japan Translation Federation.

(you can read a longer bio here)

MTSummitXVI_JTFWorkshop_4_HirokiKawano.pdf

Abstract

There are three important elements to benefit from the introduction of machine translation in the field of industrial translation: to bring out active cooperation of translators, to apply to fields suitable for machine translation, and to apply the quality assurance process corresponding to post edit.

機械翻訳の導入によって産業翻訳の分野で利益を得るためには、以下の三点が重要であることを示します:翻訳者の積極的な協力を引き出すこと、機械翻訳に適した分野に適用すること、そしてポストエディットに対応する品質保証プロセスを適用すること。

How Translation Industry Should Adopt Machine Translation

Gen Sato, SDL Japan

Gen Sato has been in Localization from 1999. He started as a translator and then experienced various roles including a reviewer, a vendor manager, and a language team manager. In 2009, he joined SDL Japan as a sales reps of translation software. He became a sales manager in 2016 and since then he has been proactively involved in Localization industry events to share his views toward its future.

(you can read a longer bio here)

MTSummitXVI_JTFWorkshop_5_GenSato.pdf

Abstract

Although expectation for machine translation is very high, it is unrealistic to think machine translation can replace human translation completely. In this presentation, I will define pros and cons of machine translation and examine what would be the best scenario for both clients and translation industry.

機械翻訳への期待が高まっていますが、これまで翻訳業界が行ってきた作業を全て機械翻訳に置き換えるということはできません。この発表では機械翻訳が得意なことと不得意なことを定義して、翻訳の発注側と受注側の両方にとって機械翻訳を効率的に使用する方法を考察します。