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-21259@www.clsp.jhu.edu DTSTAMP:20240329T064247Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nNatural language processin g has been revolutionized by neural networks\, which perform impressively well in applications such as machine translation and question answering. D espite their success\, neural networks still have some substantial shortco mings: Their internal workings are poorly understood\, and they are notori ously brittle\, failing on example types that are rare in their training d ata. In this talk\, I will use the unifying thread of hierarchical syntact ic structure to discuss approaches for addressing these shortcomings. Firs t\, I will argue for a new evaluation paradigm based on targeted\, hypothe sis-driven tests that better illuminate what models have learned\; using t his paradigm\, I will show that even state-of-the-art models sometimes fai l to recognize the hierarchical structure of language (e.g.\, to conclude that “The book on the table is blue” implies “The table is blue.”) Second\ , I will show how these behavioral failings can be explained through analy sis of models’ inductive biases and internal representations\, focusing on the puzzle of how neural networks represent discrete symbolic structure i n continuous vector space. I will close by showing how insights from these analyses can be used to make models more robust through approaches based on meta-learning\, structured architectures\, and data augmentation.
\nBiography
\nTom McCoy is a PhD candidate in the Department of Cognitive Science at Johns Hopkins University. As an undergr aduate\, he studied computational linguistics at Yale. His research combin es natural language processing\, cognitive science\, and machine learning to study how we can achieve robust generalization in models of language\, as this remains one of the main areas where current AI systems fall short. In particular\, he focuses on inductive biases and representations of lin guistic structure\, since these are two of the major components that deter mine how learners generalize to novel types of input.
DTSTART;TZID=America/New_York:20220131T120000 DTEND;TZID=America/New_York:20220131T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Tom McCoy (Johns Hopkins University) “Opening the Black Box of Deep Learning: Representations\, Inductive Biases\, and Robustness” URL:https://www.clsp.jhu.edu/events/tom-mccoy-johns-hopkins-university-open ing-the-black-box-of-deep-learning-representations-inductive-biases-and-ro bustness/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,January\,McCoy END:VEVENT BEGIN:VEVENT UID:ai1ec-21275@www.clsp.jhu.edu DTSTAMP:20240329T064247Z 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
\nAs AI-driven lan guage interfaces (such as chat-bots) become more integrated into our lives \, they need to become more versatile and reliable in their communication with human users. How can we make progress toward building more “general” models that are capable of understanding a broader spectrum of language co mmands\, given practical constraints such as the limited availability of l abeled data?
\nIn this talk\, I will describe my research toward addressing this question along two dimensions of generality. First I will discuss progress in “breadth” — models that address a wider variety of tasks and abilities\, drawing inspiration from existing statistical le arning techniques such as multi-task learning. In particular\, I will show case a system that works well on several QA benchmarks\, resulting in stat e-of-the-art results on 10 benchmarks. Furthermore\, I will show its exten sion to tasks beyond QA (such as text generation or classification) that c an be “defined” via natural language. In the second part\, I will focus o n progress in “depth” — models that can handle complex inputs such as comp ositional questions. I will introduce Text Modular Networks\, a general fr amework that casts problem-solving as natural language communication among simpler “modules.” Applying this framework to compositional questions by leveraging discrete optimization and existing non-compositional closed-box QA models results in a model with strong empirical performance on multipl e complex QA benchmarks while providing human-readable reasoning.
\nI will conclude with future research directions toward broader N LP systems by addressing the limitations of the presented ideas and other missing elements needed to move toward more general-purpose interactive la nguage understanding systems.
\nBiography
\nDaniel Khashabi is a postdoctoral researcher at the Al len Institute for Artificial Intelligence (AI2)\, Seattle. Previously\, he completed his Ph.D. in Computer and Information Sciences at the Universit y of Pennsylvania in 2019. His interests lie at the intersection of artifi cial intelligence and natural language processing\, with a vision toward m ore general systems through unified algorithms and theories.
DTSTART;TZID=America/New_York:20220218T120000 DTEND;TZID=America/New_York:20220218T131500 LOCATION:Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j /96735183473 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Allen Institute for Artificial Intelligence) “The Quest Toward Generality in Natural Language Understanding” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-allen-institute-for-art ificial-intelligence/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,February\,Khashabi END:VEVENT BEGIN:VEVENT UID:ai1ec-22412@www.clsp.jhu.edu DTSTAMP:20240329T064247Z 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-23302@www.clsp.jhu.edu DTSTAMP:20240329T064247Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230130T120000 DTEND;TZID=America/New_York:20230130T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Fried (CMU) URL:https://www.clsp.jhu.edu/events/daniel-fried-cmu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Fried\,January END:VEVENT BEGIN:VEVENT UID:ai1ec-23886@www.clsp.jhu.edu DTSTAMP:20240329T064247Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:
Abstract
\nThe arms race to build inc reasingly larger\, powerful language models (LMs) in the past year has bee n remarkable. Yet incorporating LMs effectively into practical application s that facilitate manual workflows remains challenging. I will discuss LMs ’ limiting factors and our efforts to overcome them. I will start with cha llenges surrounding efficient and robust LM alignment. I will share insigh ts from our recent paper “Sel f-Instruct” (ACL 2023)\, where we used vanilla (unaligned) LMs for ali gning itself\, an approach that has yielded some success. Then\, I will mo ve on to the challenge of tracing the output of LMs to reliable sources\, a weakness that makes them prone to hallucinations. I will discuss our rec ent approach of ‘according-to’ prom pting\, which steers LMs to quote directly from sources observed in it s pre-training. If time permits\, I will discuss our ongoing project to ad apt LMs to interact with web pages. Throughout the presentation\, I will h ighlight our progress\, and end with questions about our future progress.< /p>\n
Biography
\nDaniel Khashabi is an assistant professor in computer science at Johns Hopkins University and the Center for Language and Speech Processing (CLSP) member. He is interested in bui lding reasoning-driven modular NLP systems that are robust\, transparent\, and communicative\, particularly those that use natural language as the c ommunication medium. Khashabi has published over 40 papers on natural lang uage processing and AI in top-tier venues. His work touches upon developin g. His research has won the ACL 2023 Outstanding Paper Award\, NAACL 2022 Best Paper Award\, research gifts from the Allen Institute for AI\, and an Amazon Research Award 2023. Before joining Hopkins\, he was a postdoctora l fellow at the Allen Institute for AI (2019-2022) and obtained a Ph.D. fr om the University of Pennsylvania in 2019.
DTSTART;TZID=America/New_York:20230908T120000 DTEND;TZID=America/New_York:20230908T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Daniel Khashabi (Johns Hopkins University) “Building More Helpful L anguage Models” URL:https://www.clsp.jhu.edu/events/daniel-khashabi-johns-hopkins-universit y/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,Khashabi\,September END:VEVENT BEGIN:VEVENT UID:ai1ec-24239@www.clsp.jhu.edu DTSTAMP:20240329T064247Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nNon-in vasive neural interfaces have the potential to transform human-computer in teraction by providing users with low friction\, information rich\, always available inputs. Reality Labs at Meta is developing such an interface fo r the control of augmented reality devices based on electromyographic (EMG ) signals captured at the wrist. Speech and audio technologies turn out to be especially well suited to unlocking the full potential of these signal s and interactions and this talk will present several specific problems an d the speech and audio approaches that have advanced us towards this ultim ate goal of effortless and joyful interfaces. We will provide the necessar y neuroscientific background to understand these signals\, describe automa tic speech recognition-inspired interfaces generating text and beamforming -inspired interfaces for identifying individual neurons\, and then explain how they connect with egocentric machine intelligence tasks that might re side on these devices.
\nBiography
\nMichael I Mandel is a Research Scientist in Reality Labs at Meta. Previously\, he was an Associate Professor of Computer and Information Science at Brooklyn College and the CUNY Graduate Center working at the intersection of machi ne learning\, signal processing\, and psychoacoustics. He earned his BSc i n Computer Science from the Massachusetts Institute of Technology and his MS and PhD with distinction in Electrical Engineering from Columbia Univer sity as a Fu Foundation Presidential Scholar. He was an FQRNT Postdoctoral Research Fellow in the Machine Learning laboratory (LISA/MILA) at the Uni versité de Montréal\, an Algorithm Developer at Audience Inc\, and a Resea rch Scientist in Computer Science and Engineering at the Ohio State Univer sity. His work has been supported by the National Science Foundation\, inc luding via a CAREER award\, the Alfred P. Sloan Foundation\, and Google\, Inc.
DTSTART;TZID=America/New_York:20240129T120000 DTEND;TZID=America/New_York:20240129T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Michael I Mandel (Meta) “Speech and Audio Processing in Non-Invasiv e Brain-Computer Interfaces at Meta” URL:https://www.clsp.jhu.edu/events/michael-i-mandel-cuny/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,January\,Mandel END:VEVENT END:VCALENDAR