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-21275@www.clsp.jhu.edu DTSTAMP:20240328T213601Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:
Abstract
\n\n\n\n\nAutomatic discovery of phon e or word-like units is one of the core objectives in zero-resource speech processing. Recent attempts employ contrastive predictive coding (CPC)\, where the model learns representations by predicting the next frame given past context. However\, CPC only looks at the audio signal’s structure at the frame level. The speech structure exists beyond frame-level\, i.e.\, a t phone level or even higher. We propose a segmental contrastive predictiv e coding (SCPC) framework to learn from the signal structure at both the f rame and phone levels.\n\n\nSCPC is a hierarchical model with three stages trained in an end-to-end m anner. In the first stage\, the model predicts future feature frames and e xtracts frame-level representation from the raw waveform. In the second st age\, a differentiable boundary detector finds variable-length segments. I n the last stage\, the model predicts future segments to learn segment rep resentations. Experiments show that our model outperforms existing phone a nd word segmentation methods on TIMIT and Buckeye datasets.
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 END:VCALENDAR