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-20115@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).\nBiography\nJiri Mekyska is the head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology\, wher e he leads a multidisciplinary team of researchers (signal processing engi neers\, data scientists\, neurologists\, psychologists) with a special foc us on the development of new digital endpoints and digital biomarkers enab ling to better understand\, diagnose and monitor neurodegenerative (e.g. P arkinson’s disease) and neurodevelopmental (e.g. dysgraphia) diseases. DTSTART;TZID=America/New_York:20210329T120000 DTEND;TZID=America/New_York:20210329T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Jiri Mekyska (Brno University of Technology) “Data Science in Small Medical Data Sets: From Logistic Regression Towards Logistic Regression” URL:https://www.clsp.jhu.edu/events/jiri-mekyska-brno-university-of-technol ogy/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nData science in small medical datasets usually means doing precision guesswork on unreliable data provided by those with high e xpectations. The first part of this talk will focus on issues that data sc ientists and engineers have to address when working with this kind of data (e.g. unreliable labels\, the effect of confounding factors\, necessity o f clinical interpretability\, difficulties with fusing more data sets). Th e second part of the talk will include some real examples of this kind of data science in the field of neurology (prediction of motor deficits in Pa rkinson’s disease based on acoustic analysis of speech\, diagnosis of Park inson’s disease dysgraphia utilising online handwriting\, exploring the Mo zart effect in epilepsy based on the music information retrieval) and psyc hology (assessment of graphomotor disabilities in children with developmen tal dysgraphia).
\nBiography
\nAbstr act
\nNeural sequence generation systems oftentimes generat e sequences by searching for the most likely sequence under the learnt pro bability distribution. This assumes that the most likely sequence\, i.e. t he mode\, under such a model must also be the best sequence it has to offe r (often in a given context\, e.g. conditioned on a source sentence in tra nslation). Recent findings in neural machine translation (NMT) show that t he true most likely sequence oftentimes is empty under many state-of-the-a rt NMT models. This follows a large list of other pathologies and biases o bserved in NMT and other sequence generation models: a length bias\, large r beams degrading performance\, exposure bias\, and many more. Many of the se works blame the probabilistic formulation of NMT or maximum likelihood estimation. We provide a different view on this: it is mode-seeking search \, e.g. beam search\, that introduces many of these pathologies and biases \, and such a decision rule is not suitable for the type of distributions learnt by NMT systems. We show that NMT models spread probability mass ove r many translations\, and that the most likely translation oftentimes is a rare event. We further show that translation distributions do capture imp ortant aspects of translation well in expectation. Therefore\, we advocate for decision rules that take into account the entire probability distribu tion and not just its mode. We provide one example of such a decision rule \, and show that this is a fruitful research direction.
\nBi ography
\nI am an assistant professor (UD) in natu ral language processing at the Institute for Logic\, Language and Computation where I lead the Probabilistic Language Learning group.
\nMy work concerns the design of models and algorithms that learn to represe nt\, understand\, and generate language data. Examples of specific problem s I am interested in include language modelling\, machine translation\, sy ntactic parsing\, textual entailment\, text classification\, and question answering.
\nI also develop techniques to approach general machine l earning problems such as probabilistic inference\, gradient and density es timation.
\nMy interests sit at the intersection of disciplines such as statistics\, machine learning\, approximate inference\, global optimiz ation\, formal languages\, and computational linguistics.
\n\n< p> \n X-TAGS;LANGUAGE=en-US:2021\,April\,Aziz END:VEVENT BEGIN:VEVENT UID:ai1ec-20120@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nRobotics@Google’s mission is to make robots useful in the real world through machine learning. We are excited about a new model for robotics\, designed for generalization across diverse environments an d instructions. This model is focused on scalable data-driven learning\, w hich is task-agnostic\, leverages simulation\, learns from past experience \, and can be quickly adapted to work in the real-world through limited in teractions. In this talk\, we’ll share some of our recent work in this dir ection in both manipulation and locomotion applications.\nBiography\nCarol ina Parada is a Senior Engineering Manager at Google Robotics. She leads t he robot-mobility group\, which focuses on improving robot motion planning \, navigation\, and locomotion\, using reinforcement learning. Prior to th at\, she led the camera perception team for self-driving cars at Nvidia fo r 2 years. She was also a lead with Speech @ Google for 7 years\, where sh e drove multiple research and engineering efforts that enabled Ok Google\, the Google Assistant\, and Voice-Search. Carolina grew up in Venezuela an d moved to the US to pursue a B.S. and M.S. degree in Electrical Engineeri ng at University of Washington and her Phd at Johns Hopkins University at the Center for Language and Speech Processing (CLSP). DTSTART;TZID=America/New_York:20210423T120000 DTEND;TZID=America/New_York:20210423T131500 LOCATION:via Zoom SEQUENCE:0 SUMMARY:Carolina Parada (Google AI) “State of Robotics @ Google” URL:https://www.clsp.jhu.edu/events/carolina-parada-google-ai/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n
Abstr act
\nRobotics@Google’s mission is to make robots useful i n the real world through machine learning. We are excited about a new mode l for robotics\, designed for generalization across diverse environments a nd instructions. This model is focused on scalable data-driven learning\, which is task-agnostic\, leverages simulation\, learns from past experienc e\, and can be quickly adapted to work in the real-world through limited i nteractions. In this talk\, we’ll share some of our recent work in this di rection in both manipulation and locomotion applications.
\n< strong>Biography
\nCarolina Parad a is a Senior Engineering Manager at Google Robotics. She leads the robot-mobility group\, which focuses on improving robot motion planning\, navigation\, and locomotion\, using reinforcement learning. Prior to that \, she led the camera perception team for self-driving cars at Nvidia for 2 years. She was also a lead with Speech @ Google for 7 years\, where she drove multiple research and engineering efforts that enabled Ok Google\, t he Google Assistant\, and Voice-Search. Carolina< /span> grew up in Venezuela and moved to the US to pursue a B.S. and M.S. degree in Electrical Engineering at University of Washington and her Phd a t Johns Hopkins University at the Center for Language and Speech Processin g (CLSP).
\n X-TAGS;LANGUAGE=en-US:2021\,April\,Parada END:VEVENT BEGIN:VEVENT UID:ai1ec-23515@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\n\n\nHow important are different temporal speech mod ulations for speech recognition? We answer this question from two compleme ntary perspectives. Firstly\, we quantify the amount of phonetic informati on in the modulation spectrum of speech by computing the mutual informatio n between temporal modulations with frame-wise phoneme labels. Looking fro m another perspective\, we ask – which speech modulations an Automatic Spe ech Recognition (ASR) system prefers for its operation. Data-driven weight s are learned over the modulation spectrum and optimized for an end-to-end ASR task. Both methods unanimously agree that speech information is mostl y contained in slow modulation. Maximum mutual information occurs around 3 -6 Hz which also happens to be the range of modulations most preferred by the ASR. In addition\, we show that the incorporation of this knowledge in to ASRs significantly reduces their dependency on the amount of training d ata.\n DTSTART;TZID=America/New_York:20230403T120000 DTEND;TZID=America/New_York:20230403T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Samik Sadhu (JHU) “Importance of Different Tempor al Modulations of Speech: A Tale of Two Perspectives” URL:https://www.clsp.jhu.edu/events/student-seminar-samik-sadhu/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nHow important are different temporal speech modulations for speec h recognition? We answer this question from two complementary perspectives . Firstly\, we quantify the amount of phonetic information in the modulati on spectrum of speech by computing the mutual information between temporal modulations with frame-wise phoneme labels. Looking from another perspect ive\, we ask – which speech modulations an Automatic Speech Recognition (A SR) system prefers for its operation. Data-driven weights are learned over the modulation spectrum and optimized for an end-to-end ASR task. Both me thods unanimously agree that speech information is mostly contained in slo w modulation. Maximum mutual information occurs around 3-6 Hz which also h appens to be the range of modulations most preferred by the ASR. In additi on\, we show that the incorporation of this knowledge into ASRs significan tly reduces their dependency on the amount of training data.
\n< p> \nLearning How to Play With The Machines: Taking Stock of Where the Collaboration Between Computational and Social Science Stands
\n\n
Speakers: Jeff Gill\, Ernesto Calvo\, Hale Sirin and Antonios Anastasopoulos
\n X-TAGS;LANGUAGE=en-US:2023\,April\,APSA Roundtable END:VEVENT BEGIN:VEVENT UID:ai1ec-23586@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230410T120000 DTEND;TZID=America/New_York:20230410T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Ruizhe Huang URL:https://www.clsp.jhu.edu/events/student-seminar-ruizhe-huang/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Huang END:VEVENT BEGIN:VEVENT UID:ai1ec-23588@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAdvances in open domain Large Language Models (LLMs) starting with BERT and more recently with GPT-4\, PaLM\, and LLaMA have fa cilitated dramatic improvements in conversational systems. These improveme nts include an unprecedented breadth of conversational interactions betwee n humans and machines while maintaining and sometimes surpassing the accur acy of systems trained specifically for known\, closed domains. However\, many applications still require higher levels of accuracy than pre-trained LLMs can provide. There are many studies underway to accomplish this. Bro adly speaking\, the methods assume the pre-trained models are fixed (due t o cost/time)\, and instead look to various augmentation methods including prompting strategies and model adaptation/fine-tuning.\nOne augmentation s trategy leverages the context of the conversation. For example\, who are t he participants and what is known about these individuals (personal contex t)\, what was just said (dialogue context)\, where is the conversation tak ing place (geo context)\, what time of day and season is it (time context) \, etc. A powerful form of context is the shared visual setting of the co nversation between the human(s) and machine. The shared visual scene may b e from a device (phone\, smart glasses) or represented on a screen (browse r\, maps\, etc.) The elements in the visual context can be exploited by gr ounding the natural language conversational interaction\, thereby changing the priors of certain concepts and increasing the accuracy of the system. In this talk\, I will present some of my historical work in this area as well as my recent work in the AI Virtual Assistant (AVA) Lab at Georgia Te ch.\nBio\nDr. Larry Heck is a Professor with a joint appointment in the Sc hool of Electrical and Computer Engineering and the School of Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Fa rmer Distinguished Chair of Advanced Computing Concepts and is a Georgia R esearch Alliance Eminent Scholar. His received the BSEE from Texas Tech Un iversity (1986)\, and MSEE and PhD EE from the Georgia Institute of Techno logy (1989\,1991). He is a Fellow of the IEEE\, inducted into the Academy of Distinguished Engineering Alumni at Georgia Tech and received the Disti nguished Engineer Award from the Texas Tech University Whitacre College of Engineering. He was a Senior Research Engineer with SRI (1992-98)\, Vice President of R&D at Nuance (1998-2005)\, Vice President of Search and Adve rtising Sciences at Yahoo! (2005-2009)\, Chief Scientist of the Microsoft Speech products and Distinguished Engineer in Microsoft Research (2009-201 4)\, Principal Scientist with Google Research (2014-2017)\, and CEO of Viv Labs and SVP at Samsung (2017-2021).\n\n DTSTART;TZID=America/New_York:20230414T120000 DTEND;TZID=America/New_York:20230414T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Larry Heck (Georgia Institute of Technology) “The AVA Digital Human : Improving Conversational Interactions through Visually Situated Context” URL:https://www.clsp.jhu.edu/events/larry-heck-georgia-institute-of-technol ogy-the-ava-digital-human-improving-conversational-interactions-through-vi sually-situated-context/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAdvances in open domain Large Lan guage Models (LLMs) starting with BERT and more recently with GPT-4\, PaLM \, and LLaMA have facilitated dramatic improvements in conversational syst ems. These improvements include an unprecedented breadth of conversational interactions between humans and machines while maintaining and sometimes surpassing the accuracy of systems trained specifically for known\, closed domains. However\, many applications still require higher levels of accur acy than pre-trained LLMs can provide. There are many studies underway to accomplish this. Broadly speaking\, the methods assume the pre-trained mod els are fixed (due to cost/time)\, and instead look to various augmentatio n methods including prompting strategies and model adaptation/fine-tuning.
\nOne augmentation strategy leverages the conte xt of the conversation. For example\, who are the participants and what is known about these individuals (personal context)\, what was just said (di alogue context)\, where is the conversation taking place (geo context)\, w hat time of day and season is it (time context)\, etc. A powerful form of context is the shared visual setting of the conversation between the huma n(s) and machine. The shared visual scene may be from a device (phone\, sm art glasses) or represented on a screen (browser\, maps\, etc.) The elemen ts in the visual context can be exploited by grounding the natural languag e conversational interaction\, thereby changing the priors of certain conc epts and increasing the accuracy of the system. In this talk\, I will pres ent some of my historical work in this area as well as my recent work in t he AI Virtual Assistant (AVA) Lab at Georgia Tech.
\nBio
\nDr. Larry Heck is a Professor with a joi nt appointment in the School of Electrical and Computer Engineering and th e School of Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Distinguished Chair of Advanced Computing Con cepts and is a Georgia Research Alliance Eminent Scholar. His received the BSEE from Texas Tech University (1986)\, and MSEE and PhD EE from the Geo rgia Institute of Technology (1989\,1991). He is a Fellow of the IEEE\, in ducted into the Academy of Distinguished Engineering Alumni at Georgia Tec h and received the Distinguished Engineer Award from the Texas Tech Univer sity Whitacre College of Engineering. He was a Senior Research Engineer wi th SRI (1992-98)\, Vice President of R&D at Nuance (1998-2005)\, Vice Pres ident of Search and Advertising Sciences at Yahoo! (2005-2009)\, Chief Sci entist of the Microsoft Speech products and Distinguished Engineer in Micr osoft Research (2009-2014)\, Principal Scientist with Google Research (201 4-2017)\, and CEO of Viv Labs and SVP at Samsung (2017-2021).
\n\n
\n X-TAGS;LANGUAGE=en-US:2023\,April\,Heck END:VEVENT BEGIN:VEVENT UID:ai1ec-23590@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nMachine Translation has the ultimate goal of eliminat ing language barriers. However\, the area has focused mainly on a few lang uages\, leaving many low-resource languages without support. In this talk\ , I will discuss the challenges of bringing translation support for 200 wr itten languages and beyond.\n\nFirst\, I talk about the No Language Left B ehind Project\, where we took on this challenge by first contextualizing t he need for low-resource language translation support through exploratory interviews with native speakers. Then\, we created datasets and models aim ed at narrowing the performance gap between low and high-resource language s. We proposed multiple architectural and training improvements to counter act over-fitting while training on thousands of language-pairs/tasks. We e valuated the performance of over 40\,000 different translation directions. \n\nAfterwards\, I’ll discuss the challenges of pushing translation perfor mance beyond text for languages that don’t have written standards like Hok kien.\nOur models achieve state-of-the-art performance and lay important g roundwork towards realizing a universal translation system. At the same ti me\, we keep making open-source contributions for everyone to keep advanci ng the research for the languages they care about.\nBio\nPaco is Research Scientist Manager supporting translation teams in Meta AI (FAIR). He works in the field of machine translation with a focus on low-resource translat ion (e.g. NLLB\, FLORES) and the aim to break language barriers. He joined Meta in 2016. His research has been published in top-tier NLP venues like ACL\, EMNLP. He was the co-chair of the Research director at AMTA (2020-2 022). He has ave organized several research competitions focused on low-re source translation and data filtering. Paco obtained his PhD from the ITES M in Mexico\, was a visiting scholar at the LTI-CMU from 2008-2009\, and p articipated in DARPA’s GALE evaluation program. Paco was a post-doc and sc ientist at Qatar Computing Research Institute in Qatar in 2012-2016 DTSTART;TZID=America/New_York:20230417T120000 DTEND;TZID=America/New_York:20230417T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Paco Guzman (Meta AI) “Building a Universal Translation System to B reak Down Language Barriers” URL:https://www.clsp.jhu.edu/events/paco-guzman-meta-ai/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\nAbstr act
\nOur models achieve state-of-the-art performance and lay important groundwork towards realizing a universal translation system. At the same time\, we keep maki ng open-source contributions for everyone to keep advancing the research f or the languages they care about.
\nBio
\nPac o is Research Scientist Manager supporting translation teams in Meta AI (F AIR). He works in the field of machine translation with a focus on low-res ource translation (e.g. NLLB\, FLORES) and the aim to break language barri ers. He joined Meta in 2016. His research has been published in top-tier N LP venues like ACL\, EMNLP. He was the co-chair of the Research director a t AMTA (2020-2022). He has ave organized several research competitions foc used on low-resource translation and data filtering. Paco obtained his PhD from the ITESM in Mexico\, was a visiting scholar at the LTI-CMU from 200 8-2009\, and participated in DARPA’s GALE evaluation program. Paco was a p ost-doc and scientist at Qatar Computing Research Institute in Qatar in 20 12-2016
\n X-TAGS;LANGUAGE=en-US:2023\,April\,Guzman END:VEVENT BEGIN:VEVENT UID:ai1ec-23592@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nLarge language models (LLMs) have ushered in exciting capabilities in language understanding and text generation\, with systems like ChatGPT holding fluent dialogs with users and being almost indisting uishable from humans. While this has obviously raised conversational syste ms and chatbots to a new level\, it also presents exciting new opportuniti es for building artificial agents with improved decision making capabiliti es. Specifically\, the ability to reason with language can allow us to bui ld agents that can 1) execute complex action sequences to effect change in the world\, 2) learn new skills by ‘reading’ in addition to ‘doing’\, and 3) allow for easier personalization and control over their behavior. In t his talk\, I will demonstrate how we can build such language-enabled agent s that exhibit the above traits across various use cases such as multi-hop question answering\, web interaction\, and robotic tool manipulation. In the end\, I will also discuss some dangers of using these LLM-based system s and some challenges that lie ahead in ensuring their safe use.\nBiograph y\nKarthik Narasimhan is an assistant professor in the Computer Science de partment at Princeton University and a co-Director of the Princeton NLP gr oup. His research spans the areas of natural language processing and reinf orcement learning\, with the goal of building intelligent agents that lear n to operate in the world through both their own experience (”doing things ”) and leveraging existing human knowledge (”reading about things”). Karth ik received his PhD from MIT in 2017\, and spent a year as a visiting rese arch scientist at OpenAI contributing to the GPT language model\, prior to joining Princeton in 2018. His research has been recognized by the NSF CA REER\, a Google Research Scholar Award\, an Amazon research award (2019)\, Bell Labs runner-up prize and outstanding paper awards at EMNLP (2015\, 2 016) and NeurIPS (2022). DTSTART;TZID=America/New_York:20230421T120000 DTEND;TZID=America/New_York:20230421T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Karthik Narasimhan (Princeton University) ” Towards General-Purpose Language-Enabled Agents: Machines that can Read\, Think and Act” URL:https://www.clsp.jhu.edu/events/karthik-narasimhan-princeton-university / X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nLarge language models (LLMs) have ushered in exciting capabilities in language understanding and text generation\, with systems like ChatGPT holding fluent dialogs with users and being almost indisting uishable from humans. While this has obviously raised conversational syste ms and chatbots to a new level\, it also presents exciting new opportuniti es for building artificial agents with improved decision making capabiliti es. Specifically\, the ability to reason with language can allow us to bui ld agents that can 1) execute complex action sequences to effect change in the world\, 2) learn new skills by ‘reading’ in addition to ‘doing’\, and 3) allow for easier personalization and control over their behavior. In t his talk\, I will demonstrate how we can build such language-enabled agent s that exhibit the above traits across various use cases such as multi-hop question answering\, web interaction\, and robotic tool manipulation. In the end\, I will also discuss some dangers of using these LLM-based system s and some challenges that lie ahead in ensuring their safe use.
\n< strong>Biography
\nKarthik Narasimhan is an assistan t professor in the Computer Science department at Princeton University and a co-Director of the Princeton NLP group. His research spans the areas of natural language processing and reinforcement learning\, with the goal of building intelligent agents that learn to operate in the world through bo th their own experience (”doing things”) and leveraging existing human kno wledge (”reading about things”). Karthik received his PhD from MIT in 2017 \, and spent a year as a visiting research scientist at OpenAI contributin g to the GPT language model\, prior to joining Princeton in 2018. His rese arch has been recognized by the NSF CAREER\, a Google Research Scholar Awa rd\, an Amazon research award (2019)\, Bell Labs runner-up prize and outst anding paper awards at EMNLP (2015\, 2016) and NeurIPS (2022).
\n X-TAGS;LANGUAGE=en-US:2023\,April\,Narasimhan END:VEVENT BEGIN:VEVENT UID:ai1ec-23606@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20230424T120000 DTEND;TZID=America/New_York:20230424T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Brian Lu URL:https://www.clsp.jhu.edu/events/student-seminar-brian-lu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2023\,April\,Lu END:VEVENT BEGIN:VEVENT UID:ai1ec-23608@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\nAutomated analysis of student writing has the potenti al to provide alternatives to selected-response questions such as multiple choice\, and to enable teachers and instructors to assess students’ reaso ning skills based on their long-form writing. Further\, automated support to assess both short answers and long passages could provide students with a smoother trajectory towards mastery of written communication. Our meth ods focus on the specific ideas students express to support formative asse ssment through different kinds of feedback\, which aims to scaffold their abilities to reason and communicate. In this talk I review our work in the PSU NLP lab on methods for automated assessment of different forms of stu dent writing\, from younger and older students. I will briefly illustrate highly curated datasets created in collaboration with researchers in STEM education\, results from deployment of an older content analysis tool on middle school physics essays\, and very preliminary results on assessment of college students’ physics lab reports. I will also present our current work on short answer assessment using a novel recurrent relation network that incorporates contrastive learning.\nBio\nBecky Passonneau has been a Professor in the Department of Computer Science and Engineering at Penn St ate University since 2016\, when she joined as the first NLP researcher. S ince that time the NLP faculty has grown to include Rui Zhang and Wenpeng Yin. Becky’s research in natural language processing addresses computation al pragmatics\, meaning the investigation of language as a system of inter active behavior that serves a wide range of purposes. She received her PhD in Linguistics from the University of Chicago in 1985\, and worked at sev eral academic and industry research labs before joining Penn State. Her wo rk is reported in over 140 publications in journals and refereed conferenc e proceedings\, and has been funded through 27 sponsored projects from 16 sources\, including government agencies\, corporate sponsors\, corporate gifts\, and foundations.. DTSTART;TZID=America/New_York:20230428T120000 DTEND;TZID=America/New_York:20230428T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Becky Passonneau (Penn State University) ” Automated Support to Sca ffold Students’ Short- and Long-form STEM Writing” URL:https://www.clsp.jhu.edu/events/becky-passonneau-penn-state-university- automated-support-to-scaffold-students-short-and-long-form-stem-writing/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nAutomated analysis of student writing has the potenti al to provide alternatives to selected-response questions such as multiple choice\, and to enable teachers and instructors to assess students’ reaso ning skills based on their long-form writing. Further\, automated support to assess both short answers and long passages could provide students with a smoother trajectory towards mastery of written communication. Our meth ods focus on the specific ideas students express to support formative asse ssment through different kinds of feedback\, which aims to scaffold their abilities to reason and communicate. In this talk I review our work in the PSU NLP lab on methods for automated assessment of different forms of stu dent writing\, from younger and older students. I will briefly illustrate highly curated datasets created in collaboration with researchers in STEM education\, results from deployment of an older content analysis tool on middle school physics essays\, and very preliminary results on assessment of college students’ physics lab reports. I will also present our current work on short answer assessment using a novel recurrent relation network that incorporates contrastive learning.
\nBio
\nBecky Passonneau has been a Professor in the Department of Computer Sci ence and Engineering at Penn State University since 2016\, when she joined as the first NLP researcher. Since that time the NLP faculty has grown to include Rui Zhang and Wenpeng Yin. Becky’s research in natural language p rocessing addresses computational pragmatics\, meaning the investigation o f language as a system of interactive behavior that serves a wide range of purposes. She received her PhD in Linguistics from the University of Chic ago in 1985\, and worked at several academic and industry research labs be fore joining Penn State. Her work is reported in over 140 publications in journals and refereed conference proceedings\, and has been funded through 27 sponsored projects from 16 sources\, including government agencies\, corporate sponsors\, corporate gifts\, and foundations..
\n X-TAGS;LANGUAGE=en-US:2023\,April\,Passonneau END:VEVENT BEGIN:VEVENT UID:ai1ec-24491@www.clsp.jhu.edu DTSTAMP:20240328T083007Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240401T120000 DTEND;TZID=America/New_York:20240401T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Yuan Gong URL:https://www.clsp.jhu.edu/events/yuan-gong/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Gong END:VEVENT END:VCALENDAR