Translation Tools Could Save Less-Used Languages

AITopics Original Links

Sometimes you may feel like there's nothing worth reading on the Web, but at least there's plenty of material you can read and understand. Millions of people around the world, in contrast, speak languages that are still barely represented online, despite widespread Internet access and improving translation technology.

He Said, She Said: Addressing Gender in Neural Machine Translation Slator


Artificial intelligence technology has run into a potentially delicate issue: gender bias. In November 2018, mainstream news media reported that Google's automatic suggestion tool for Google Mail will not suggest gender-based pronouns to avoid autocompleting a sentence with the wrong gender. The feature (called Smart Compose) will avoid suggesting genders because, as Gmail Product Manager Paul Lambert put it, "not all'screw-ups' are equal…[gender is] a big, big thing." Google Translate, which now largely runs on neural machine translation (NMT), had also recently addressed the question of gender bias. On December 6, 2018, Google published a first blog post about its efforts to reduce gender bias in Google Translate.

Estimating Word Translation Probabilities from Unrelated Monolingual Corpora Using the EM Algorithm

AAAI Conferences

Selecting the right word translation among several options in the lexicon is a core problem for machine translation. We present a novel approach to this problem that can be trained using only unrelated monolingual corpora and a lexicon. By estimating word translation probabilities using the EM algorithm, we extend upon target language modeling. We construct a word translation model for 3830 German and 6147 English noun tokens, with very promising results.

Microsoft Research Asia (MSRA) Leads in 2019 WMT International Machine Translation Competition


Microsoft Research Asia (MSRA) has achieved eight top places in the recent machine translation challenge organized by the 2019 fourth Conference on Machine Translation (WMT19), out of the eleven tasks it undertook. Overall, there are nineteen machine translation categories in WMT this year. MSRA achieved first place in machine translation tasks for Chinese-English, English-Finnish, English-German, English-Lithuanian, French-German, German-English, German-French and Russian-English. Three other tasks were placed second in their respective categories, which included English-Kazakh, Finnish-English and Lithuanian-English. As one of the leading machine translation competition globally, WMT is a platform for leading researchers to demonstrate their solutions, as well as to understand the continuous evolvement of machine translation technology.