BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-22412@www.clsp.jhu.edu DTSTAMP:20240329T150441Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nDriven by the goal of erad icating language barriers on a global scale\, machine translation has soli dified itself as a key focus of artificial intelligence research today. Ho wever\, such efforts have coalesced around a small subset of languages\, l eaving behind the vast majority of mostly low-resource languages. What doe s it take to break the 200 language barrier while ensuring safe\, high-qua lity results\, all while keeping ethical considerations in mind? In this t alk\, I introduce No Language Left Behind\, an initiative to break languag e barriers for low-resource languages. In No Language Left Behind\, we too k on the low-resource language translation challenge by first contextualiz ing the need for translation support through exploratory interviews with n ative speakers. Then\, we created datasets and models aimed at narrowing t he performance gap between low and high-resource languages. We proposed mu ltiple architectural and training improvements to counteract overfitting w hile training on thousands of tasks. Critically\, we evaluated the perform ance of over 40\,000 different translation directions using a human-transl ated benchmark\, Flores-200\, and combined human evaluation with a novel t oxicity benchmark covering all languages in Flores-200 to assess translati on safety. Our model achieves an improvement of 44% BLEU relative to the p revious state-of-the-art\, laying important groundwork towards realizing a universal translation system in an open-source manner.
\nBi ography
\nAngela is a research scientis t at Meta AI Research in New York\, focusing on supporting efforts in spee ch and language research. Recent projects include No Language Left Behind (https://ai.facebook.com/r esearch/no-language-left-behind/) and Universal Speech Translation for Unwritten Languages (https://ai.faceb ook.com/blog/ai-translation-hokkien/). Before translation\, Angela pre viously focused on research in on-device models for NLP and computer visio n and text generation.
\nDTSTART;TZID=America/New_York:20221118T120000 DTEND;TZID=America/New_York:20221118T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Angela Fan (Meta AI Research) “No Language Left Behind: Scaling Hu man-Centered Machine Translation” URL:https://www.clsp.jhu.edu/events/angela-fan-facebook/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Fan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-22422@www.clsp.jhu.edu DTSTAMP:20240329T150441Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nZipf’s law is commonly glo ssed by the aphorism “infrequent words are frequent\,” but in practice\, i t has often meant that there are three types of words: frequent\, infreque nt\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (with dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV word s was not solved until sequence-to-sequence neural nets de-reified the con cept of a word. Many other social phenomena follow power-law distribution s. The number of native speakers of the N’th most spoken language\, for e xample\, is 1.44 billion over N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingu al pre-training. In less-frequent languages\, multilingual knowledge tran sfer can significantly reduce phone error rates. In languages with no tra ining data\, unsupervised ASR methods can be proven to converge\, as long as the eigenvalues of the language model are sufficiently well separated t o be measurable. Other systems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech pat terns that were never seen in the training database\, but not all disabili ties need do so. The inability of speech technology to work for people wi th even common disabilities is probably caused by a lack of data\, and can probably be solved by finding better modes of interaction between technol ogy researchers and the communities served by technology.
\nBiography
\nMark Hasegawa-Johnson is a William L. Everitt F aculty Fellow of Electrical and Computer Engineering at the University of Illinois in Urbana-Champaign. He has published research in speech product ion and perception\, source separation\, voice conversion\, and low-resour ce automatic speech recognition.
DTSTART;TZID=America/New_York:20221209T120000 DTEND;TZID=America/New_York:20221209T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Mark Hasegawa-Johnson (University of Illinois Urbana-Champaign) “Zi pf’s Law Suggests a Three-Pronged Approach to Inclusive Speech Recognition ” URL:https://www.clsp.jhu.edu/events/mark-hasegawa-johnson-university-of-ill inois-urbana-champaign/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,December\,Hasegawa-Johnson END:VEVENT END:VCALENDAR