18th Workshop on Innovative Use of NLP for Building Educational Applications

toronto

Quick Info
Co-located with	ACL 2023
Location	Toronto, Canada
Deadline	~~April 24,~~ May 2, 2023
Date	July 13, 2023
Organizers	Ekaterina Kochmar, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Nitin Madnani, Anaïs Tack, Victoria Yaneva, Zheng Yuan, and Torsten Zesch
Contact	bea.nlp.workshop@gmail.com

Workshop Description

The BEA Workshop is a leading venue for NLP innovation in the context of educational applications. It is one of the largest one-day workshops in the ACL community with over 100 registered attendees in the past several years. The growing interest in educational applications and a diverse community of researchers involved resulted in the creation of the Special Interest Group in Educational Applications (SIGEDU) in 2017, which currently has over 300 members.

The workshop’s continuing growth highlights the alignment between societal needs and technological advances: for instance, BEA16 in 2021 hosted a panel discussion on New Challenges for Educational Technology in the Time of the Pandemic addressing the pressing issues around COVID19. NLP capabilities can now support an array of learning domains, including writing, speaking, reading, science, and mathematics, as well as the related intra-personal (e.g., self-confidence) and inter-personal (e.g., peer collaboration) skills. Within these areas, the community continues to develop and deploy innovative NLP approaches for use in educational settings. Another breakthrough for educational applications within the CL community is the presence of a number of shared-task competitions organized by the BEA workshop over the past several years, including four shared tasks on grammatical error detection and correction alone. NLP/Education shared tasks have also seen new areas of research, such as the Automated Evaluation of Scientific Writing at BEA11, Native Language Identification at BEA12, Second Language Acquisition Modelling at BEA13, and Complex Word Identification at BEA13. These competitions increased the visibility of, and interest in, our field.

The 18th BEA workshop will follow the format of BEA in 2022 and will be hybrid. We will have three invited talks, a shared task on generation of teacher responses in educational dialogues, oral presentation sessions, and a large poster session to maximize the amount of original work presented. We expect that the workshop will continue to highlight novel technologies and opportunities, including the use of state-of-the-art large language models in educational applications, and challenges around responsible AI for educational NLP, in English as well as other languages. The workshop will solicit both full papers and short papers for either oral or poster presentation. We will solicit papers that incorporate NLP methods, including, but not limited to: automated scoring of open-ended textual and spoken responses; game-based instruction and assessment; educational data mining; intelligent tutoring; peer review; grammatical error detection and correction; learner cognition; spoken dialog; multimodal applications; tools for teachers and test developers; and use of corpora. We will solicit papers that incorporate NLP methods, including, but not limited to:

automated scoring of open-ended textual and spoken responses;
automated scoring/evaluation for written student responses (across multiple genres);
game-based instruction and assessment;
educational data mining;
intelligent tutoring;
collaborative learning environments;
peer review;
grammatical error detection and correction;
learner cognition;
spoken dialog;
multimodal applications;
annotation standards and schemas;
tools and applications for classroom teachers, learners and/or test developers; and
use of corpora in educational tools.

Workshop Program

Invited Talks

The workshop will have two keynote presentations and one ambassador paper presentation from EDM 2022 (The 15th International Conference on Educational Data Mining), a member society of the IAALDE (International Alliance to Advance Learning in the Digital Era).

S. Lottridge (Cambium Assessment)

Susan Lottridge, Cambium Assessment
Building Educational Applications using NLP: A Measurement Perspective

Abstract: The domains of NLP, data science, software engineering, and educational measurement are becoming increasingly interdependent when creating NLP-based educational applications. Indeed, the domains themselves are merging in key ways, with each incorporating one another’s methods and tools into their work. For example, many software engineers regularly deploy machine learning models and many linguists, data scientists, and measurement staff regularly develop software. Even so, each discipline approaches this complex task with the assumptions, priorities, and values of their field. The best educational applications are the result of multi-disciplinary teams that can leverage one another’s strengths and can recognize and honor the values of each disciplinary perspective.
This talk will describe the educational measurement perspective within this collaborative process. At a high level, educational measurement is the design, use, and analysis of assessments in order to make inferences about what students know and can do. Given this, the measurement experts on a team focus heavily on defining what students need to know and do, what evidence supports inferences about what students know and can do, and whether the data are accurate, reliable, and fair to all students. This perspective can impact the full life-cycle development of educational applications, from designing the core product focus, data collection activities, NLP modelling, analysis of model outputs, and information provided to students. It can also help ensure that educational applications produce information that is valuable to teachers and students. Because these perspectives can be opaque to those outside of measurement, the development process of various NLP educational tools will be used to illustrate key areas where measurement can contribute in product design.

Bio: Sue Lottridge is a Chief Scientist of Natural Language Applications at Cambium Assessment, Inc. She has a Ph.D. in Assessment and Measurement from James Madison University and Masters’ degrees in Mathematics and Computer Science from the University of Wisconsin – Madison. In this role, she leads CAI’s machine learning and scoring team on the research, development, and operation of CAI’s automated scoring and feedback software. Dr. Lottridge has worked in automated scoring for fifteen years and has contributed to the design, research, and use of multiple automated scoring engines including equation scoring, essay scoring, short answer scoring, speech scoring, crisis alert detection, and essay feedback.

J. Heller (Textio)

Jordana Heller, Textio
Interrupting Linguistic Bias in Written Communication with NLP tools

Abstract: Unconscious bias is hard to detect, but when we identify it in language usage, we can take steps to interrupt and reduce it. At Textio, we focus on using NLP to detect, interrupt, and educate writers about bias in written workforce communications. Unconscious bias affects many facets of the employee lifecycle. Exclusionary language in recruiting communications can deter candidates from diverse backgrounds from even applying to a position, hindering efforts to build inclusive workplaces. Once a candidate has accepted a position, the language used to provide them feedback on their performance affects how they develop professionally, and we have found stark inequities in the language of feedback to members of different demographic groups. This talk will discuss how Textio uses NLP to interrupt these patterns of bias by assessing these texts for bias and providing 1) real-time iterative, educational feedback to the writer on how to improve a specific document, including guidance toward less-biased language alternatives, and 2) an assessment at a workplace level of exclusionary and inclusive language, so that companies can set goals around language improvement and track their progress toward them.

Bio: Jordana Heller, PhD, is Director of Data Intelligence at Textio, a tech company focused on interrupting bias in performance feedback and recruiting. Textio identifies bias in written documents and provides data to writers in real time that helps them write more effectively and equitably. At Textio, Jordana applies her background as a computational psycholinguist and cognitive scientist to her leadership of R&D teams who are focused on using data and NLP to help employers reduce bias and accelerate professional growth equitably.

A. Tack (KU Leuven)

Anaïs Tack, KU Leuven, imec
Generating Teacher Responses in Educational Dialogues: The AI Teacher Test & BEA 2023 Shared Task

Abstract: How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability.
In the first part of this talk, I will describe our paper The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialogues presented at EDM 2022. The paper reported on a first attempt at an AI teacher test. We built a solution around the insight that you can run conversational agents in parallel to human teachers in real-world dialogues, simulate how different agents would respond to a student, and compare these counterpart responses in terms of three abilities: speak like a teacher, understand a student, help a student. Our method builds on the reliability of comparative judgments in education and uses a probabilistic model and Bayesian sampling to infer estimates of pedagogical ability. We find that, even though conversational agents (Blender in particular) perform well on conversational uptake, they are quantifiably worse than real teachers on several pedagogical dimensions, especially with regard to helpfulness.
In the second part of this talk, I will describe the results of the BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues, which was a continuation of our EDM paper.

Shared Task

Webpage: https://sig-edu.org/sharedtask/2023

The workshop will host a shared task on generation of teacher responses in educational dialogues. Participants will be provided with teacher–student dialogue samples from the Teacher Student Chatroom Corpus (Caines et al., 2020) of real-world teacher–student interactions and will be asked to generate teacher responses using NLP and AI methods. Submissions will be ranked according to automated evaluation metrics, with the top submissions selected for further human evaluation. Given active participation in the previous BEA-hosted shared tasks, we expect to attract around 20 participating teams.

Organizers: Anaïs Tack, KU Leuven, imec; Ekaterina Kochmar, MBZUAI; Zheng Yuan, King’s College London; Serge Bibauw, Universidad Central del Ecuador; Chris Piech, Stanford University

Accepted Papers

We received 110 submissions and accepted 58 papers (acceptance rate: 53%).

Oral Presentations

Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. Nischal Ashok Kumar, Nigel Steven Fernandez, Zichao Wang, and Andrew Lan
Grammatical Error Correction for Sentence-level Assessment in Language Learning. Anisia Katinskaia and Roman Yangarber

Poster Presentations

A Closer Look at k-Nearest Neighbors Grammatical Error Correction. Justin Vasselli and Taro Watanabe
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods. Mengsay Loem, Masahiro Kaneko, sho Takase, and Naoaki Okazaki
Gender-Inclusive Grammatical Error Correction through Augmentation. Gunnar Lund, Kostiantyn Omelianchuk, and Igor Samokhin
Recognizing Learner Handwriting Retaining Orthographic Errors for Enabling Fine-Grained Error Feedback. Christian Gold, Ronja Laarmann-Quante, and Torsten Zesch
Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks. Arianna Masciolini, Elena Volodina, and Dana Dannélls
Training for Grammatical Error Correction Entirely Without Human-annotated L2 Learners’ Corpora. Mikio Oda
A dynamic model of lexical experience for tracking of oral reading fluency. Beata Beigman Klebanov, Michael Suhan, Zuowei Wang, and Tenaha O’Reilly
ALEXSIS+: Improving Substitute Generation and Selection for Lexical Simplification with Information Retrieval. Kai North, Alphaeus Dmonte, Tharindu Ranasinghe, Matthew Shardlow, and Marcos Zampieri
CEFR-based Contextual Lexical Complexity Classifier in English and French. Desislava Aleksandrova and Vincent Pouliot
“Geen makkie”: Interpretable Classification and Simplification of Dutch Text Complexity. Eliza Hobo, Charlotte Pouw, and Lisa Beinborn
Hybrid Models for Sentence Readability Assessment. Fengkai Liu and John Lee
Japanese Lexical Complexity for Non-Native Readers: a New Dataset. Yusuke Ide, Masato Mita, Adam Nohejl, Hiroki Ouchi, and Taro Watanabe
You’ve Got a Friend in … a Language Model? A Comparison of Explanations of Multiple-Choice Items of Reading Comprehension between ChatGPT and Humans. George Duenas, Sergio Jimenez, and Geral Eduardo Mateus Ferro
ACTA: Short-Answer Grading in High-Stakes Medical Exams. King Yiu Suen, Victoria Yaneva, Le An Ha, Janet Mee, Yiyun Zhou, and Polina Harik
Automated evaluation of written discourse coherence using GPT-4. Ben Naismith, Phoebe Mulcaire, and Jill Burstein
ExASAG: Explainable Framework for Automatic Short Answer Grading. Maximilian Tornqvist, Mosleh Mahamud, Erick Mendez Guzman, and Alexandra Farazouli
Predicting the Quality of Revisions in Argumentative Writing. Zhexiong Liu, Diane Litman, Elaine Lin Wang, Lindsay C. Matsumura, and Richard Correnti
Rating Short L2 Essays on the CEFR Scale with GPT-4. Kevin P. Yancey, Geoffrey T. LaFlair, Anthony R. Verardi, and Jill Burstein
Span Identification of Epistemic Stance-Taking in Academic Written English. Masaki Eguchi and Kristopher Kyle
Towards Extracting and Understanding the Implicit Rubrics of Transformer Based Automatic Essay Scoring Models. James Fiacco, David Adamson, and Carolyn Rosé
Transformer-based Hebrew NLP models for Short Answer Scoring in Biology. Abigail Gurin Schleifer, Beata Beigman Klebanov, Moriah Ariely, and Giora Alexandron
Analyzing Bias in Large Language Model Solutions for Assisted Writing Feedback Tools: Lessons from the Feedback Prize Competition Series. Perpetual Baffour, Tor Saxberg, and Scott Crossley
Labels are not necessary: Assessing peer-review helpfulness using domain adaptation based on self-training. Chengyuan Liu, Divyang Doshi, Muskaan Bhargava, Ruixuan Shang, Jialin Cui, Dongkuan Xu, and Edward Gehringer
Reviewriter: AI-Generated Instructions For Peer Review Writing. Xiaotian Su, Roman Rietsche, Seyed Parsa Neshaei, Thiemo Wambsganss, and Tanja Käser
Assisting Language Learners: Automated Trans-Lingual Definition Generation via Contrastive Prompt Learning. Hengyuan Zhang, Dawei Li, Yanran Li, Chenming Shang, Chufan Shi, and Yong Jiang
Comparing Neural Question Generation Architectures for Reading Comprehension. E. Margaret Perkoff, Abhidip Bhattacharyya, Jon Z. Cai, and Jie Cao
Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory. Masaki Uto, Yuto Tomikawa, and Ayaka Suzuki
Generating Better Items for Cognitive Assessments Using Large Language Models. Antonio Laverghetta Jr., and John Licato
Generating Dialog Responses with Specified Grammatical Items for Second Language Learning. Yuki Okano, Kotaro Funakoshi, Ryo Nagata and Manabu Okumura
Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning. Semere Kiros Bitew, Johannes Deleu, A. Seza Doğruöz, Chris Develder, and Thomas Demeester
MultiQG-TI: Towards Question Generation from Multi-modal Sources. Zichao Wang and Richard Baraniuk
Using Learning Analytics for Adaptive Exercise Generation. Tanja Heck and Detmar Meurers
GrounDialog: A Dataset for Repair and Grounding in Task-oriented Spoken Dialogues for Language Learning. Xuanming Zhang, Rahul Divekar, Rutuja Ubale, and Zhou Yu
SIGHT: A Large Annotated Dataset on Student Insights Gathered from Higher Education Transcripts. Rose Wang, Pawan Wirawarn, Noah Goodman, and Dorottya Demszky
Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations. Erfan Al-Hossami, Razvan Bunescu, Ryan Teehan, Laurel Powell, Khyati Mahajan, and Mohsen Dorodchi
Towards L2-friendly pipelines for learner corpora: A case of written production by L2-Korean learners. Hakyung Sung and Gyu-Ho Shin
Improving Mathematics Tutoring With A Code Scratchpad. Shriyash Upadhyay, Etan Ginsberg, and Chris Callison-Burch
Scalable and Explainable Automated Scoring for Open-Ended Constructed Response Math Word Problems. Scott Hellman, Alejandro Andrade, and Kyle Habermehl
The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts. Dorottya Demszky and Heather Hill
Does BERT Exacerbate Gender or L1 Biases in Automated English Speaking Assessment? Alexander Kwako, Yixin Wan, Jieyu Zhao, Mark Hansen, Kai-Wei Chang, and Li Cai
Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home. Eda Okur, Roddy Fuentes Alba, Saurav Sahay, and Lama Nachman
Exploring a New Grammatico-functional Type of Measure as Part of a Language Learning Expert System. Cyriel Mallart, Andrew Simpkin, Rémi Venant, Nicolas Ballier, Bernardo Stearns, Jen Yu Li, and Thomas Gaillat
Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction. Rose Wang and Dorottya Demszky
Reconciling Adaptivity and Task Orientation in the Student Dashboard of an Intelligent Language Tutoring System. Leona Colling, Tanja Heck, and Detmar Meurers
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Survey Generation. Irene Li, Thomas George, Alex Fabbri, Tammy Liao, Benjamin Chen, Rina Kawamura, Richard Zhou, Vanessa Yan, Swapnil Hingmire, and Dragomir Radev
Enhancing Human Summaries for Question-Answer Generation in Education. Hannah Gonzalez, Liam Dugan, Eleni Miltsakaki, Zhiqi Cui, Jiaxuan Ren, Bryan Li, Shriyash Upadhyay, Etan Ginsberg, and Chris Callison-Burch
Automatically Generated Summaries of Video Lectures Enhance Students’ Learning Experience. Hannah Gonzalez, Jiening Li, Helen Jin, Jiaxuan Ren, Hongyu Zhang, Ayotomiwa Akinyele, Adrian Wang, Eleni Miltsakaki, Ryan Baker, and Chris Callison-Burch
Handcrafted Features in Computational Linguistics. Bruce W. Lee and Jason Lee

Demonstrations

ChatBack: Investigating Methods of Providing Grammatical Error Feedback in a GUI-based Language Learning Chatbot. Kai-Hui Liang, Sam Davidson, Xun Yuan, Shehan Panditharatne, Chun-Yen Chen, Ryan Shea, Derek Pham, Yinghua Tan, Erik Voss, Luke Fryer, and Zhou Yu
Enhancing Video-based Learning Using Knowledge Tracing: Personalizing Students’ Learning Experience with ORBITS. Shady Shehata, David Santandreu Calonge, Philip Purnell, and Mark Thompson
Evaluating Classroom Potential for Card-it: Digital Flashcards for Studying and Learning Italian Morphology. Mariana Shimabukuro, Jessica Zipf, Shawn Yama, and Christopher Collins
ReadAlong-Studio Web Interface for Digital Interactive Storytelling. Aidan Pine, David Huggins-Daines, Eric Joanis, Patrick Littell, Marc Tessier, Delasie Torkornoo, Rebecca Knowles, Roland Kuhn, and Delaney Lothian
UKP-SQuARE: An Interactive Tool for Teaching Question Answering. Haishuo Fang, Haritz Puerto, and Iryna Gurevych
Auto-req: Automatic detection of pre-requisite dependencies between academic videos. Rushil Thareja, Ritik Garg, Shiva Baghel, Deep Dwivedi, Mukesh Mohania, and Ritvik Kulshrestha
Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications. Changrong Xiao, Sean Xin Xu, Kunpeng Zhang, Yufang Wang, and Lei Xia
Beyond Black Box AI generated Plagiarism Detection: From Sentence to Document Level. Chunhui Li, Parijat Dube, and Ali Quidwai

Special Track: Shared Task Posters

To be announced soon.

Anti-Harassment Policy

SIGEDU adheres to the ACL Anti-Harassment Policy for the BEA workshops. Any participant of the workshop who experiences harassment or hostile behavior may contact any current member of the ACL Executive Committee or contact Priscilla Rasmussen, who is usually available at the registration desk of the conference. Please be assured that if you approach us, your concerns will be kept in strict confidence, and we will consult with you on any actions taken.

If you are interested in sharing your code and data with the BEA community, we created the #bea-workshop topic on GitHub.

Important Dates

Note: All deadlines are 11:59pm UTC-12 (anywhere on earth).

Anonymity Period Begins: Friday, March 24, 2023
Submission Deadline: ~~Monday, April 24~~ Tuesday, May 2, 2023
Notification of Acceptance: ~~Monday, May 22~~ Tuesday, May 23, 2023
Camera-ready Papers Due: ~~Tuesday, May 30~~ Wednesday, May 31, 2023
Pre-recorded Videos Due: Monday, June 12, 2023
Workshop: Thursday, July 13, 2023

Registration

If you wish to attend the workshop, you must register with the ACL conference here. Select BEA from the list of offered workshops. There is no need to have a paper accepted. The workshop is open to anyone who wishes to attend. Importantly, at least one author of each accepted paper must register.

Visa information

All visa requests are processed by the ACL’s dedicated visa assistance team. To apply for an invitation letter, please follow the information from https://2023.aclweb.org/blog/visa-info/. Specifically, for invitation letters and further questions, it is advised that you reach out at acl2023_visa_help@googlegroups.com, communicating the following: name, institution, address, role in conference (author/reviewer/workshop organizer/invited speakers/other participants), additional reasons/justifications for attending. ACL’s dedicated visa assistance team will be in touch to help out with the visa letter request.

ACL 2023 invitation letter form can be found here.

Submission Information

We will be using the ACL Submission Guidelines for the BEA Workshop this year. Authors are invited to submit a long paper of up to eight (8) pages of content, plus unlimited references; final versions of long papers will be given one additional page of content (up to 9 pages) so that reviewers’ comments can be taken into account. We also invite short papers of up to four (4) pages of content, plus unlimited references. Upon acceptance, short papers will be given five (5) content pages in the proceedings. Authors are encouraged to use this additional page to address reviewers’ comments in their final versions. Papers which describe systems are also invited to give a demo of their system. If you would like to present a demo in addition to presenting the paper, please make sure to select either “long paper + demo” or “short paper + demo” under “Submission Category” in the START submission page.

Previously published papers cannot be accepted. The submissions will be reviewed by the program committee. As reviewing will be blind, please ensure that papers are anonymous. Self-references that reveal the author’s identity, e.g., “We previously showed (Smith, 1991) …”, should be avoided. Instead, use citations such as “Smith previously showed (Smith, 1991) …”.

We have also included conflict of interest in the submission form. You should mark all potential reviewers who have been authors on the paper, are from the same research group or institution, or who have seen versions of this paper or discussed it with you.

Note on Ethics Statement and Limitations sections: We welcome submissions with Ethics Statement and Limitations sections included. These sections will not count towards the page limit in accordance with ACL guidelines. This year, such sections are not yet mandatory and papers missing them will not be penalized. Next year, we will include more detailed information on these sections in our submission guidelines.

We will be using the START conference system to manage submissions: https://www.softconf.com/acl2023/bea2023/

LaTeX and Word Templates

Paper submissions must use the official ACL style templates, which are available from here. Please follow the paper formatting guidelines general to “*ACL” conferences available here. Authors may not modify these style files or use templates designed for other conferences. Submissions that do not conform to the required styles, including paper size, margin width, and font size restrictions, will be rejected without review.

Double Submission Policy

We will follow the official ACL double-submission policy. Specifically, papers being submitted both to BEA and another conference or workshop must:

Note on the title page (as a footnote on the abstract) the other conference or workshop to which they are being submitted.
State on the title page that if the authors choose to present their paper at BEA (assuming it was accepted), then the paper will be withdrawn from other conferences and workshops.

Organizing Committee

Ekaterina Kochmar, MBZUAI
Jill Burstein, Duolingo
Andrea Horbach, FernUniversität in Hagen
Ronja Laarmann-Quante, Ruhr University Bochum
Nitin Madnani, ETS
Anaïs Tack, KU Leuven, imec
Victoria Yaneva, National Board of Medical Examiners
Zheng Yuan, King’s College London
Torsten Zesch, FernUniversität in Hagen

Workshop contact email address: bea.nlp.workshop@gmail.com

Program Committee

Sihat Afnan, Bangladesh University of Engineering and Technology
Tazin Afrin, Educational Testing Service
Erfan Al-Hossami, University of North Carolina at Charlotte
Desislava Aleksandrova, Université de Montréal
Aderajew Alem, Wollo University Ethiopia
Giora Alexandron, Weizmann Institute of Science
David Alfter, Université catholique de Louvain
Alejandro Andrade, Pearson Knowledge Technologies
Nischal Ashok Kumar, University of Massachusetts Amherst
Berk Atil, Pennsylvania State University
Rabin Banjade, University of Memphis
Michael Gringo Angelo Bayona, Trinity College Dublin
Lee Becker, Pearson
Beata Beigman Klebanov, Educational Testing Service
Marie Bexte, FernUniversität in Hagen
Abhidip Bhattacharyya, University of Colorado Boulder
Serge Bibauw, Universidad Central del Ecuador; KU Leuven
Shayekh Bin Islam, Bangladesh University of Engineering and Technology
Daniel Brenner, ETS
Ted Briscoe, MBZUAI
Dominique Brunato, Institute of Computational Linguistics / CNR (Pisa, Italy)
Chris Callison-Burch, University of Pennsylvania
Hannan Cao, National University of Singapore
Jie Cao, University of Colorado Boulder
Brian Carpenter, Indiana University of Pennsylvania
Dumitru-Clementin Cercel, University Politehnica of Bucharest
Guanliang Chen, Monash University
Li-Hsin Chang, University of Turku
Hyundong Cho, USC ISI
Martin Chodorow, The City University of New York
Aubrey Condor, University of California, Berkeley
Mark Core, University of Southern California
Steven Coyne, Tohoku University
Scott Crossley, Vanderbilt University
Sam Davidson, University of California, Davis
Kordula De Kuthy, University of Tübingen
Jasper Degraeuwe, Ghent University
Thomas Demeester, Internet Technology and Data Science Lab (IDLab), Ghent University
Carrie Demmans Epp, University of Alberta
Dorottya Demszky, Stanford University
Yuning Ding, FernUniversität in Hagen
Rahul Divekar, ETS
George Duenas, Universidad Pedagogica Nacional
Masaki Eguchi, University of Oregon
Yo Ehara, Tokyo Gakugei University
Mariano Felice, British Council
Wanyong Feng, UMass Amherst
Nigel Steven Fernandez, University of Massachusetts Amherst
James Fiacco, Carnegie Mellon University
Michael Flor, Educational Testing Service
Estibaliz Fraca, University College London
Kotaro Funakoshi, Tokyo Institute of Technology
Thomas Gaillat, University of Rennes 2
Ananya Ganesh, University of Colorado Boulder
Lingyu Gao, TTIC
Rujun Gao, Texas A&M University
Ritik Garg, Extramarks Education India
Christian Gold, FernUniversität in Hagen
Samuel González-López, Technological University of Nogales Sonora
Le An Ha, University of Wolverhampton
Ching Nam Hang, City University of Hong Kong
Nicolas Hernandez, Nantes University - LS2N
Chung-Chi Huang, Frostburg State University
Ping-Yu Huang, The General Education Center, Ming Chi University of Technology
Yi-Ting Huang, NTUST
David Huggins-Daines, independent researcher
Yusuke Ide, NAIST
Joseph Marvin Imperial, University of Bath
Radu Tudor Ionescu, University of Bucharest
Qinjin Jia, North Carolina State University
Helen Jin, University of Pennsylvania
Richard Johansson, University of Gothenburg
Masahiro Kaneko, Tokyo Institute of Technology
Neha Kardam, University of Washington
Anisia Katinskaia, University of Helsinki
Elma Kerz, RWTH Aachen University
Mamoru Komachi, Tokyo Metropolitan University
Roland Kuhn, National Research Council of Canada
Alexander Kwako, UCLA
Kristopher Kyle, University of Oregon
Geoffrey LaFlair, Duolingo
Antonio Laverghetta Jr., University of South Florida
Ji-Ung Lee, UKP Lab, TU Darmstadt, Germany
Jaewook Lee, UMass Amherst
Arun Balajiee Lekshmi Narayanan, University of Pittsburgh
Xu Li, Zhejiang University
Chengyuan Liu, North Carolina State University
Yudong Liu, Western Washington University
Zhexiong Liu, University of Pittsburgh
Zoey Liu, University of Florida
Susan Lottridge, Cambium Assessment
Anastassia Loukina, Grammarly Inc.
Jiaying Lu, Emory University
Jakub Macina, ETH Zurich
Lieve Macken, Ghent University, Belgium
James Martin, University of Colorado Boulder
Sandeep Mathias, Presidency University, Bangalore
Janet Mee, NBME
Detmar Meurers, University of Tübingen
Phoebe Mulcaire, Duolingo
Tsegay Mullu, Wachemo University
Faizan E Mustafa, QUIBIQ GmbH
Farah Nadeem, World Bank
Ben Naismith, Duolingo
Sungjin Nam, ACT Inc.
Diane Napolitano, Associated Press
Kamel Nebhi, Education First
Seyed Parsa Neshaei, Sharif University of Technology
Hwee Tou Ng, National University of Singapore
Huy Nguyen, Amazon
Gebregziabihier Nigusie, Mizan-Tepi University
S Jaya Nirmala, National Institute of Technology, Tiruchirapalli
Kai North, George Mason University
Eda Okur, Intel Labs
Priti Oli, University of Memphis
Kostiantyn Omelianchuk, Grammarly
Brian Ondov, National Library of Medicine
Christopher Ormerod, Cambium Assessment
Simon Ostermann, Deutsches Forschungszentrum für Künstliche Intelligenz
Ulrike Pado, Hochschule fuer Technik Stuttgart
Frank Palma Gomez, CUNY Queens College
Chanjun Park, Upstage AI
Rebecca Passonneau, Penn State
Fabio Perez, University of Campinas
E. Margaret Perkoff, University of Colorado Boulder
Jakob Prange, Hong Kong Polytechnic University
Reinald Adrian Pugoy, University of the Philippines
Long Qin, Alibaba Cloud
Mengyang Qiu, University at Buffalo
Muhammad Reza Qorib, National University of Singapore
Martí Quixal, University of Tübingen
Arjun Ramesh Rao, Microsoft
Vivi Rantung, Universitas Negeri Manado
Manav Rathod, Glean
Brian Riordan, ETS
Frankie Robertson, University of Jyväskylä
Aiala Rosá, Instituto de Computación, Facultad de Ingeniería, Udelar
Carolyn Rosé, Carnegie Mellon University
Alla Rozovskaya, Queens College, CUNY
Igor Samokhin, Grammarly
Alexander Scarlatos, UMass Amherst
Matthew Shardlow, Manchester Metropolitan University
Anchal Sharma
Shady Shehata, MBZUAI
Gyu-Ho Shin, Palacký University Olomouc
Shashank Sonkar, Rice University
Katherine Stasaski, Salesforce Research
Helmer Strik, Radboud University
Hakyung Sung, University of Oregon
Abhijit Suresh, Reddit Inc.
Jan Švec, University of West Bohemia
Xiangru Tang, Yale University
Zhongwei Teng, Vanderbilt University Nashville
Rushil Thareja, Extramarks Education
Naveen Thomas, Texas A&M University
Alexandra Uitdenbogerd, RMIT
Shriyash Upadhyay, Martian
Masaki Uto, The University of Electro-communications
Sowmya Vajjala, National Research Council, Canada
Justin Vasselli, Nara Institute of Science and Technology
Giulia Venturi, Institute for Computational Linguistics “A. Zampolli” (CNR-ILC)
Anthony Verardi, Duolingo
Carl Vogel, Trinity College Dublin
Elena Volodina, University of Gothenburg, Sweden
Spencer von der Ohe, University of Alberta
Zichao Wang, Rice University
Taro Watanabe, Nara Institute of Science and Technology
Michael White, The Ohio State University
Alistair Willis, The Open University
Man Fai Wong, City University of Hong Kong
Menbere Worku
Changrong Xiao, Tsinghua University
Yiqiao Xu, North Carolina State University
Kevin P. Yancey, Duolingo
Roman Yangarber, University of Helsinki
Su-Youn Yoon, EduLab
Kamyar Zeinalipour, University of Siena
Hengyuan Zhang, Tsinghua University
Jing Zhang, Emory University
Jessica Zipf, University of Konstanz
Michael Zock, CNRS, (LIF) university of Aix-Marseille

18th Workshop on Innovative Use of NLP for Building Educational Applications

Workshop Description