Originally published on Kirti Vashee’s blog eMpTy Pages. Lilt is an interactive and adaptive computer-aided translation tool that integrates machine translation, translation memories, and termbases into one interface that learns from translators. Using Lilt is an entirely different experience from post-editing machine translations — an experience that our users love, and one that yields substantial productivity gains without compromising quality. The first step toward using this new kind of tool is to understand how interactive and adaptive machine assistance is different from conventional MT, and how these technologies relate to exciting new developments in neural MT and deep learning. Interactive MT doesn’t just translate each segment once and leave the translator to clean up the mess. Instead, each word that the translator types into the Lilt environment is integrated into a new automatic translation suggestion in real time. While text messaging apps autocomplete words, interactive MT autocompletes whole sentences. Interactive MT actually improves translation quality. In conventional MT post-editing, the computer knows what segment will be translated, but doesn’t know anything about the phrasing decisions that a translator will make. Interactive translations are more accurate because they can observe what the translator has typed so far and update their suggestions based on all available information.
Originally posted on LinkedIn by Greg Rosner. I saw the phrase “linguistic janitorial work” in this Deloitte whitepaper on “AI-augmented government, using cognitive technologies to redesign public sector work”, used to describe the drudgery of translation work that so many translators are required to do today through Post-editing of Machine Translation. And then it hit me what’s really going on. The sad reality over the past several years is that many professional linguists, who have decades of particular industry experience, expertise in professional translation and have earned degrees in writing, whose jobs have been reduced to sentence-by-sentence clean-up of translations that flood out of Google Translate or other Machine Translation (MT) systems.