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-20723@www.clsp.jhu.edu DTSTAMP:20240329T151543Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon.\n Biography\n\nJessy Li is an assistant professor in the Department of Lingu istics at UT Austin where she works on in computational linguistics and na tural language processing. Her work focuses on discourse processing\, text generation\, and language pragmatics in social media. She received her Ph .D. in 2017 from the University of Pennsylvania. She received an ACM SIGSO FT Distinguished Paper Award at FSE 2019\, an Area Chair Favorite at COLIN G 2018\, and a Best Paper nomination at SIGDIAL 2016.\nWeb: https://jessyl i.com DTSTART;TZID=America/New_York:20210917T120000 DTEND;TZID=America/New_York:20210917T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Jessy Li (University of Texas at Austin – Virtual Visit) “New Chall enges in Text Simplification” URL:https://www.clsp.jhu.edu/events/jessy-li-university-of-texas-at-austin/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nText simplification aims to help audiences read and u nderstand a piece of text through lexical\, syntactic\, and discourse modi fications\, while remaining faithful to its central idea and meaning. Than ks to large-scale parallel corpora derived from Wikipedia and News\, much of modern-day text simplification research focuses on sentence simplificat ion\, transforming original\, more complex sentences into simplified versi ons. In this talk\, I present new frontiers that focus on discourse operat ions. First\, we consider the challenging task of simplifying highly techn ical language\, in our case\, medical texts. We introduce a new corpus of parallel texts in English comprising technical and lay summaries of all pu blished evidence pertaining to different clinical topics. We then propose a new metric to quantify stylistic differentiates between the two\, and mo dels for paragraph-level simplification. Second\, we present the first dat a-driven study of inserting elaborations and explanations during simplific ation\, and illustrate the richness and complexities of this phenomenon. p>\n
Biography
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\nSpeech data is notoriously difficult t o work with due to a variety of codecs\, lengths of recordings\, and meta- data formats. We present Lhotse\, a speech data representation library tha t draws upon lessons learned from Kaldi speech recognition toolkit and bri ngs its concepts into the modern deep learning ecosystem. Lhotse provides a common JSON description format with corresponding Python classes and dat a preparation recipes for over 30 popular speech corpora. Various datasets can be easily combined together and re-purposed for different tasks. The library handles multi-channel recordings\, long recordings\, local and clo ud storage\, lazy and on-the-fly operations amongst other features. We int roduce Cut and CutSet concepts\, which simplify common data wrangling task s for audio and help incorporate acoustic context of speech utterances. Fi nally\, we show how Lhotse leverages PyTorch data API abstractions and ado pts them to handle speech data for deep learning.
\nB iography
\nPiotr Zelasko is an assistant research scientist in the Center for Language and Speech Processing (CLSP) who specializes i n automatic speech recognition (ASR) and spoken language understanding (SL U). His current research focuses on applying multilingual and crosslingual speech recognition systems to categorize the phonetic inventory of a prev iously unknown language and on improving defenses against adversarial atta cks on both speaker identification and automatic speech recognition system s. He is also addressing the question of how to structure a spontaneous co nversation into high-level semantic units such as dialog acts or topics. F inally\, he is working on Lhotse + K2\, the next-generation speech process ing research software ecosystem. Before joining Johns Hopkins\, Zelasko wo rked as a machine learning consultant for Avaya (2017-2019)\, and as a mac hine learning engineer for Techmo (2015-2017). Zelasko received his PhD (2 019) in electronics engineering\, as well as his master’s (2014) and under graduate degrees (2013) in acoustic engineering from AGH University of Sci ence and Technology in Kraków\, Poland.
\n X-TAGS;LANGUAGE=en-US:2021\,October\,Zelasko END:VEVENT BEGIN:VEVENT UID:ai1ec-21615@www.clsp.jhu.edu DTSTAMP:20240329T151543Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\n\nWe consider a problem of data collection for sema ntically rich NLU tasks\, where detailed semantics of documents (or uttera nces) are captured using a complex meaning representation. Previously\, d ata collection for such tasks was either handled at the cost of extensive annotator training (e.g. in FrameNet or PropBank) or simplified meaning re presentation (e.g. in QA-SRL or Overnight). In this talk\, we present two systems [1\, 2] that aim to support fast\, accurate\, and expressive sema ntic annotations by pairing human workers with a trained model in the loop .\n\nThe first system\, called Guided K-best [1]\, is an annotation toolki t for conversational semantic parsing. Instead of typing annotations from scratch\, data specialists choose a correct parse from the K-best output of a few-shot prototyped model. As the K-best list can be large (e.g. K=1 00)\, we guide the annotators’ exploration of the K-best list via explaina ble hierarchical clustering. In addition\, we experiment with RoBERTa-bas ed reranking of the K-best list to recalibrate the few-shot model towards Accuracy@K. The final system allows to annotate data up to 35% faster tha n the standard\, non-guided K-best and improves the few-shot model’s top-1 accuracy by up to 18%. The second system\, called SchemaBlocks [2]\, is an annotation toolkit for schemas\, or structured descriptions of frequent real-world scenarios (e.g.\, cooking a meal). It represents schemas in t he annotation UI as nested blocks. Using a novel Causal ARM model\, we fu rther speed up the annotation process and guide data specialists towards e xpressive and diverse schemas. As part of this work\, we collect 232 sche mas\, evaluating their internal coherence and their coverage on large-scal e newswire corpora.\n\n\n DTSTART;TZID=America/New_York:20220311T120000 DTEND;TZID=America/New_York:20220311T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Student Seminar – Anton Belyy “Systems for Human-AI Cooperation on Collecting Semantic Annotations” URL:https://www.clsp.jhu.edu/events/student-seminar-anton-belyy-systems-for -human-ai-cooperation-on-collecting-semantic-annotations/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\n\n X-TAGS;LANGUAGE=en-US:2022\,Belyy\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-23882@www.clsp.jhu.edu DTSTAMP:20240329T151543Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.\nBiograph y\nLei Li is an Assistant Professor in Language Technology Institute at Ca rnegie Mellon University. He received Ph.D. from Carnegie Mellon Universit y School of Computer Science. He is a recipient of ACL 2021 Best Paper Awa rd\, CCF Young Elite Award in 2019\, CCF distinguished speaker in 2017\, W u Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runn er-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\ , he was a faculty member at UC Santa Barbara. Prior to that\, he founded ByteDance AI Lab in 2016 and led its research in NLP\, ML\, Robotics\, an d Drug Discovery. He launched ByteDance’s machine translation system VolcT rans and AI writing system Xiaomingbot\, serving one billion users. DTSTART;TZID=America/New_York:20230901T120000 DTEND;TZID=America/New_York:20230901T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Lei Li (Carnegie Mellon University) “Empowering Responsible Use of Large Language Models” URL:https://www.clsp.jhu.edu/events/lei-li-carnegie-mellon-university-empow ering-responsible-use-of-large-language-models/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
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\nLarge language models (LLMs) have demonstrated incred ible power\, but they also possess vulnerabilities that can lead to misuse and potential attacks. In this presentation\, we will address two fundame ntal questions regarding the responsible utilization of LLMs: (1) How can we accurately identify AI-generated text? (2) What measures can safeguard the intellectual property of LLMs? We will introduce two recent watermarki ng techniques designed for text and models\, respectively. Our discussion will encompass the theoretical underpinnings that ensure the correctness o f watermark detection\, along with robustness against evasion attacks. Fur thermore\, we will showcase empirical evidence validating their effectiven ess. These findings establish a solid technical groundwork for policymaker s\, legal professionals\, and generative AI practitioners alike.
\n< strong>Biography
\nLei Li is an Assistant Professor in Lang uage Technology Institute at Carnegie Mellon University. He received Ph.D. from Carnegie Mellon University School of Computer Science. He is a recip ient of ACL 2021 Best Paper Award\, CCF Young Elite Award in 2019\, CCF di stinguished speaker in 2017\, Wu Wen-tsün AI prize in 2017\, and 2012 ACM SIGKDD dissertation award (runner-up)\, and is recognized as Notable Area Chair of ICLR 2023. Previously\, he was a faculty member at UC Santa Barba ra. Prior to that\, he founded ByteDance AI Lab in 2016 and led its resea rch in NLP\, ML\, Robotics\, and Drug Discovery. He launched ByteDance’s m achine translation system VolcTrans and AI writing system Xiaomingbot\, se rving one billion users.
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