Sam Bowman (New York University)

When Combating Hype, Proceed with Caution (December 2, 2021, 15:00 CET)   

Summary: Researchers in NLP increasingly frame and discuss research results in ways that serve to deemphasize the field's successes, at least in part in an effort to combat the field's widespread hype. Though well-meaning, this often yields misleading or even false claims about the limits of our best technology. This is a problem, and it may be more serious than it looks: It harms our credibility in ways that can make it harder to mitigate present-day harms, from NLP deployments, like those involving discriminatory systems for content moderation or resume screening. It also limits our ability to prepare for the potentially enormous impacts of more distant future advances. This talk urges researchers to be careful about these claims and suggests some research directions and communication strategies that will make it easier to avoid or rebut them.
Bio: Sam Bowman has been on the faculty at NYU since 2016, when he completed PhD with Chris Manning and Chris Potts at Stanford. At NYU, he is a member of the Center for Data Science, the Department of Linguistics, and Courant Institute's Department of Computer Science. His research focuses on data, evaluation techniques, and modeling techniques for sentence and paragraph understanding in natural language processing, and on applications of machine learning to scientific questions in linguistic syntax and semantics. He is the senior organizer behind the GLUE and SuperGLUE benchmark competitions and he has received a 2015 EMNLP Best Resource Paper Award, a 2019 *SEM Best Paper Award, a 2017 Google Faculty Research Award, and a 2021 NSF CAREER award.

Odette Scharenborg (Delft University of Technology)

Speech Representations and Processing in Deep Neural Networks (January 13, 2022, 15:00 CET)   

Summary:
Abstract Speech recognition is the mapping of a continuous, highly variable speech signal onto discrete, abstract representations. The question of how speech is represented and processed in the human brain and in automatic speech recognition (ASR) systems, although crucial in both the field of human speech processing and the field of automatic speech processing, has historically been investigated in the two fields separately. This webinar will discuss how comparisons between humans and deep neural network (DNN)-based ASRs, and cross-fertilization of the two research fields, can provide valuable insights into the way humans process speech and improve ASR technology. Specifically, it will present results of several experiments carried out on both human listeners and DNN-based ASR systems on the representation of speech in human listeners and DNNs and on lexically-guided perceptual learning, i.e., the ability to adapt a sound category on the basis of new incoming information resulting in improved processing of subsequent information. It will explain how listeners adapt to the speech of new speakers, and will present the results of a lexically-guided perceptual study carried out on a DNN-based ASR system, similar to the human experiments. In order to investigate the speech representations and adaptation processes in the DNN-based ASR systems, activations in the hidden layers of the DNN were visualized. These visualizations revealed that DNNs use speech representations that are similar to those used by human listeners, without being explicitly taught to do so, and showed an adaptation of the phoneme categories similar to what is assumed happens in the human brain.
Bio: Odette Scharenborg is an Associate Professor and Delft Technology Fellow at Delft University of Technology working on automatic speech processing. She has an interdisciplinary background in automatic speech recognition and psycholinguistics, and uses knowledge from how humans process speech in order to develop inclusive automatic speech recognition systems that are able to recognise speech from everyone, irrespective of how they speak or the language they speak. Since 2017, she is on the Board of the International Speech Communication Association, and currently serves as Vice-President. Since 2018, she is on the IEEE Speech and Language Processing Technical Committee, and she is a Senior Associate Editor of IEEE Signal Processing Letters.

Roger Moore (The University of Sheffield)

Talking with Robots: Are We Nearly There Yet? (February 3, 2022, 15:00 CET)   

Summary:
Recent years have seen considerable progress in the deployment of 'intelligent' communicative agents such as Apple's Siri and Amazon’s Alexa. However, effective speech-based human-robot dialogue is less well developed; not only do the fields of robotics and spoken language technology present their own special problems, but their combination raises an additional set of issues. In particular, there appears to be a large gap between the formulaic behaviour that typifies contemporary spoken language dialogue systems and the rich and flexible nature of human-human conversation. As a consequence, we still seem to be some distance away from creating Autonomous Social Agents such as robots that are truly capable of conversing effectively with their human counterparts in real world situations. This talk will address these issues and will argue that we need to go far beyond our current capabilities and understanding if we are to move from developing robots that simply talk and listen to evolving intelligent communicative machines that are capable of entering into effective cooperative relationships with human beings.
Bio: Prof. Moore has over 40 years’ experience in Speech Technology R&D and, although an engineer by training, much of his research has been based on insights from human speech perception and production. As Head of the UK Government's Speech Research Unit from 1985 to 1999, he was responsible for the development of the Aurix range of speech technology products and the subsequent formation of 20/20 Speech Ltd. Since 2004 he has been Professor of Spoken Language Processing at the University of Sheffield, and also holds Visiting Chairs at Bristol Robotics Laboratory and University College London Psychology & Language Sciences. He was President of the European/International Speech Communication Association from 1997 to 2001, General Chair for INTERSPEECH-2009 and ISCA Distinguished Lecturer during 2014-15. In 2017 he organised the first international workshop on ‘Vocal Interactivity in-and-between Humans, Animals and Robots (VIHAR)’. Prof. Moore is the current Editor-in-Chief of Computer Speech & Language and in 2016 he was awarded the LREC Antonio Zampoli Prize for "Outstanding Contributions to the Advancement of Language Resources & Language Technology Evaluation within Human Language Technologies” and in 2020 he was given the International Speech Communication Association Special Service Medal for "service in the establishment, leadership and international growth of ISCA".

 

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Desmond Elliott (University of Copenhagen)

Title to be announced (March 3, 2022, 15:00 CET)   

Summary:
Coming soon

 

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Dan Roth (University of Pennsylvania)

Title to be announced (April 7, 2022, 15:00 CET)   

Summary:
Coming soon

 

 

Summary:
Natural Language Processing (NLP) is at a very exciting time in its history. In the last 5 years a new technology has revolutionized the way we do our work. Even without special adaptation it tends to work better than almost every prior method, and yet we still don't really know how it works! So this is also a dangerous time: how can you trust a system that might (and sometimes does) do very strange things for which you can find no explanation or correction? In such a situation it is not a bad idea to look at the history of NLP, what NLP is at its core, and how the new technology fits into the NLP landscape. And, most importantly, where NLP is going (with or without this new technology) and how we can best prepare for it. The HiTZ Centre has a wonderful opportunity to help shape a future in which NLP will be as ubiquitous and as useful as the cellphone.
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