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-22422@www.clsp.jhu.edu DTSTAMP:20240329T101048Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nZipf’s law is commonly glossed by the aphorism “infre quent words are frequent\,” but in practice\, it has often meant that ther e are three types of words: frequent\, infrequent\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (wi th dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV words was not solved until sequ ence-to-sequence neural nets de-reified the concept of a word. Many other social phenomena follow power-law distributions. The number of native sp eakers of the N’th most spoken language\, for example\, is 1.44 billion ov er N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingual pre-training. In less-f requent languages\, multilingual knowledge transfer can significantly redu ce phone error rates. In languages with no training data\, unsupervised A SR methods can be proven to converge\, as long as the eigenvalues of the l anguage model are sufficiently well separated to be measurable. Other syst ems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech patterns that were never seen in the training database\, but not all disabilities need do so. The inabi lity of speech technology to work for people with even common disabilities is probably caused by a lack of data\, and can probably be solved by find ing better modes of interaction between technology researchers and the com munities served by technology.\nBiography\nMark Hasegawa-Johnson is a Will iam L. Everitt Faculty Fellow of Electrical and Computer Engineering at th e University of Illinois in Urbana-Champaign. He has published research i n speech production and perception\, source separation\, voice conversion\ , and low-resource 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-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nZipf’s law is commonly glossed by the aphorism “infre quent words are frequent\,” but in practice\, it has often meant that ther e are three types of words: frequent\, infrequent\, and out-of-vocabulary (OOV). Speech recognition solved the problem of frequent words in 1970 (wi th dynamic time warping). Hidden Markov models worked well for moderately infrequent words\, but the problem of OOV words was not solved until sequ ence-to-sequence neural nets de-reified the concept of a word. Many other social phenomena follow power-law distributions. The number of native sp eakers of the N’th most spoken language\, for example\, is 1.44 billion ov er N to the 1.09. In languages with sufficient data\, we have shown that monolingual pre-training outperforms multilingual pre-training. In less-f requent languages\, multilingual knowledge transfer can significantly redu ce phone error rates. In languages with no training data\, unsupervised A SR methods can be proven to converge\, as long as the eigenvalues of the l anguage model are sufficiently well separated to be measurable. Other syst ems of social categorization may follow similar power-law distributions. Disability\, for example\, can cause speech patterns that were never seen in the training database\, but not all disabilities need do so. The inabi lity of speech technology to work for people with even common disabilities is probably caused by a lack of data\, and can probably be solved by find ing better modes of interaction between technology researchers and the com munities served by technology.
\nBiography
\nMark Hasegawa-Johnson is a William L. Everitt Faculty Fellow of Electrical and Computer Engineering at the University of Illinois in Urbana-Champaig n. He has published research in speech production and perception\, source separation\, voice conversion\, and low-resource automatic speech recogni tion.
\n X-TAGS;LANGUAGE=en-US:2022\,December\,Hasegawa-Johnson END:VEVENT BEGIN:VEVENT UID:ai1ec-23304@www.clsp.jhu.edu DTSTAMP:20240329T101048Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nTransformers are essential to pretraining. As we appr oach 5 years of BERT\, the connection between attention as architecture an d transfer learning remains key to this central thread in NLP. Other archi tectures such as CNNs and RNNs have been used to replicate pretraining res ults\, but these either fail to reach the same accuracy or require supplem ental attention layers. This work revisits the semanal BERT result and con siders pretraining without attention. We consider replacing self-attention layers with recently developed approach for long-range sequence modeling and transformer architecture variants. Specifically\, inspired by recent p apers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM) and a bidirectional model architecture based on multiplicative gating. We discuss the results of th e proposed Bidirectional Gated SSM (BiGS) and present a range of analysis into its properties. Results show that architecture does seem to have a no table impact on downstream performance and a different inductive bias that is worth exploring further.\nBiography\nAlexander “Sasha” Rush is an Asso ciate Professor at Cornell Tech. His work is at the intersection of natura l language processing and generative modeling with applications in text ge neration\, efficient inference\, and controllability. He has written sever al popular open-source software projects supporting NLP research and data science\, and works part-time as a researcher at Hugging Face. He is the s ecretary of ICLR and developed software used to run virtual conferences du ring COVID. His work has received paper and demo awards at major NLP\, vis ualization\, and hardware conferences\, an NSF Career Award\, and a Sloan Fellowship. He tweets and blogs\, mostly about coding and ML\, at @srush_n lp. DTSTART;TZID=America/New_York:20230203T120000 DTEND;TZID=America/New_York:20230203T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sasha Rush (Cornell University) “Pretraining Without Attention” URL:https://www.clsp.jhu.edu/events/sasha-rush-cornell-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nTransformers are essential to pretraining. As we appr oach 5 years of BERT\, the connection between attention as architecture an d transfer learning remains key to this central thread in NLP. Other archi tectures such as CNNs and RNNs have been used to replicate pretraining res ults\, but these either fail to reach the same accuracy or require supplem ental attention layers. This work revisits the semanal BERT result and con siders pretraining without attention. We consider replacing self-attention layers with recently developed approach for long-range sequence modeling and transformer architecture variants. Specifically\, inspired by recent p apers like the structured space space sequence model (S4)\, we use simple routing layers based on state-space models (SSM) and a bidirectional model architecture based on multiplicative gating. We discuss the results of th e proposed Bidirectional Gated SSM (BiGS) and present a range of analysis into its properties. Results show that architecture does seem to have a no table impact on downstream performance and a different inductive bias that is worth exploring further.
\nBiography
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