翻譯公司E2F和機(jī)器翻譯技術(shù)公司Lilt合作
2016-06-14 10:02:25 etogether.net e2f 次
翻譯公司E2F和機(jī)器翻譯技術(shù)公司Lilt合作的首個(gè)自適應(yīng)機(jī)器翻譯大規(guī)模應(yīng)用案例
美國(guó)加利福尼亞州翻譯公司E2F本周發(fā)布成功案例，該公司15年來最大的單次翻譯項(xiàng)目：十天內(nèi)把177萬字翻譯成6種語言。此任務(wù)由有超過100個(gè)翻譯和編輯的E2F團(tuán)隊(duì)以及自適應(yīng)機(jī)器學(xué)習(xí)（ML）技術(shù)為基礎(chǔ)的公司Palo Alto-based Lilt, Inc共同完成。
Lilt公司結(jié)合自適應(yīng)機(jī)器學(xué)習(xí)（ML）的機(jī)器翻譯技術(shù)，創(chuàng)造了機(jī)器援助翻譯的新典范。該系統(tǒng)通過經(jīng)驗(yàn)，智能和人的意見進(jìn)行學(xué)習(xí)，通過共同合作，提出建議并隨著時(shí)間而改進(jìn)準(zhǔn)確度及提高翻譯效率。
結(jié)果是，譯者花費(fèi)小部分時(shí)間和成本的審閱獲得幾乎相同的翻譯質(zhì)量。這證明，機(jī)器的協(xié)助可以為客戶節(jié)省采用傳統(tǒng)人力翻譯服務(wù)的一半（或更多）的時(shí)間及費(fèi)用。
Lilt公司CEO， Spence Green，在愛爾蘭都柏林舉行的LocWorld31公布了該案例的結(jié)果，并說道： “這是有史以來第一次翻譯團(tuán)隊(duì)與機(jī)器翻譯系統(tǒng)集體訓(xùn)練，及實(shí)時(shí)性交互。該項(xiàng)目證明了自適應(yīng)機(jī)器翻譯技術(shù)具備大規(guī)模生產(chǎn)應(yīng)用”。
e2f and Lilt Case Study: First Large-Scale Application of Auto-Adaptive Machine Translation
From e2f
Combining Machine Translation (MT) with auto-adaptive Machine Learning (ML) enables a new paradigm of machine assistance. Such systems learn from the experience, intelligence and insights of their human users, improving productivity by working in partnership, making suggestions and improving accuracy over time.
The net result is that human reviewers produce far higher volumes of content, with nearly the same level of quality, for a fraction of the time and cost. Machine assistance can save customers up to one half (or more) of the price of traditional high-quality human translation services. Or, if you’ve been used to machine translation alone and have been unhappy with the results, watch your translation quality rise dramatically with a marginal increase in price.
Case Study: Travel Portal Translation
A large travel and tour web site wanted to localize 1.77 million words of content from their catalog into 6 languages within a two week window. The hard deadline was to be ready to accommodate the summer vacation plans of millions of global users with more destinations and new activities. Successfully achieving this goal required rapid mobilization of a high-quality team of humans, fully-empowered by robust machine assistance technology.
e2f, based out of San Jose, California, with over 15 years of success in the translation and localization business, provided the “human capital” for the project. Their team was comprised of 100+ experienced translators, editors, and reviewers, plus seven project managers and a senior localization engineer.
Lilt, based out of Palo Alto, California, provided the translation engine for the project. Founded in 2015, its technology platform incorporates the latest research in Natural Language Processing (NLP), Human-Computer Interaction (HCI), and Machine Learning (ML).
The Lilt platform proved invaluable in augmenting e2f’s human staff, increasingly translation speeds far beyond the industry average of 335 words per hour. The automation process required transformation of source Excel documents into a format suitable for automated processing, which were then uploaded into the correct accounts in Lilt via scripts calling Lilt’s APIs. Once translations were made within the Lilt system, output was generated and transformed back into Excel documents in the target languages.
Lilt-e2f-API
e2f’s implementation of Lilt utilized Lilt’s API, plus pre- and post-translation
processing and quality checking
Human translators could then accept or revise these segments. If Lilt’s suggestions were rejected, the approved human translations were fed back into the system, which learned from the human’s perspective and expertise. This positive feedback loop enabled faster and more accurate translation over time as the human translators contextually taught the system preferred translations of terms and phrases.
The client’s reaction was overwhelmingly positive. The number of errors was low compared to traditional machine translation solutions and the quality in line with standard human translations. Given a two-week window, the project was actually completed within 10 days.
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