April 29, 2025
dfried@andrew.cmu.edu
dpfried.github.io
Assistant Professor, Carnegie Mellon University.
2022 – present
Language Technologies Institute, School of Computer Science
Research Scientist, Meta. 2024 – present
UC Berkeley. 2015 – 2021
Ph.D. in Computer Science
Adviser: Dan Klein
Thesis: Learning Grounded Pragmatic Communication
University of Cambridge, Churchill College. 2014
– 2015
M.Phil. in Computer Science, with distinction
Adviser: Stephen Clark
Thesis: Low Rank Tensor Approximations for Compositional
Distributional Semantics
University of Arizona. 2010 – 2014
B.S. in Computer Science, Mathematics, and Information Science,
summa cum laude
Thesis Adviser: Mihai Surdeanu
Thesis: Predicting Community Traits Using the Language of Food
on Social Media
Postdoc, University of Washington. 2021 –
2022
Host: Luke Zettlemoyer
Visiting Researcher, Facebook AI Research. 2021
– 2022
Host: Mike Lewis
Research Intern, Google DeepMind. Summer
2019
Hosts: Aida Nematzadeh, Stephen Clark, Chris Dyer
Project: Structured models for language-conditioned video
segmentation
Research Intern, Microsoft Research. Summer
2016
Hosts: Hoifung Poon, Chris Quirk, Kristina Toutanova, Scott
Wen-Tau Yih
Project: Learning to rank for personalized medicine
Undergraduate Research Assistant, University of
Arizona. 2012 – 2014
Advisers: Mihai Surdeanu, Stephen Kobourov, Paul R. Cohen
Areas: Question answering, language grounding, data
visualization
Research Intern, Nara Institute of Science and
Technology. Fall 2013
Host: Kevin Duh
Project: Incorporating relational semantics in word
embeddings
Research Intern, RWTH Aachen University. Summer
2013
Hosts: Ali Jannesari, Zhen Li
Project: Supervised learning for automatic code
parallelization
Engineering Intern, Microsoft. Summer 2012
Project: Data warehousing, analytics, and visualization for
Xbox Live
| Okawa Research Award | 2023 |
| Google Ph.D. Fellowship in Natural Language Processing | 2019 – 2021 |
| Outstanding Graduate Student Instructor, UC Berkeley | 2018 |
| Outstanding Reviewer, ACL | 2018, 2020, 2021, 2022 |
| Outstanding Reviewer, NeurIPS | 2019 |
| Tencent AI Lab Fellowship | 2018 – 2019 |
| Huawei & Berkeley AI Fellowship | 2017 – 2018 |
| Best M.Phil. Student Award, Cambridge Computer Laboratory | 2015 |
| Churchill Scholarship | 2014 – 2015 |
| NDSEG Fellowship | 2014 |
| Finalist, Hertz Graduate Fellowship | 2014 |
| Outstanding Senior Award in Research, U. Arizona College of Science | 2014 |
| Outstanding Senior Award in Academics, U. Arizona Computer Science | 2014 |
| Outstanding Senior Award in Academics, U. Arizona Information Science | 2014 |
| Barry M. Goldwater Scholarship | 2013 |
| National Merit Scholar | 2010 – 2014 |
| Flinn Scholarship, Flinn Foundation of Arizona | 2010 – 2014 |
| Presidential Scholar, U.S. Department of Education | 2010 |
StarCoder: May
the Source Be With You!
Raymond Li et al. (68 authors from the BigCode Project)
Transactions on Machine Learning Research (TMLR), 2023
Human-Level
Play in the Game of Diplomacy by Combining Language Models with
Strategic Reasoning
FAIR Diplomacy Team
Science, 2022
Syntactic
Structure Distillation Pretraining for Bidirectional
Encoders
Adhiguna Kuncoro*, Lingpeng Kong*, Daniel Fried*, Dani Yogatama,
Laura Rimell, Chris Dyer, and Phil Blunsom
Transactions of the Association for Computational Linguistics
(TACL), 2020
Higher-Order
Lexical Semantic Models for Non-Factoid Answer
Reranking
Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu,
and Peter Clark
Transactions of the Association for Computational Linguistics
(TACL), 2015
Dynamic
Coalition Structure Detection in Natural Language-based
Interactions
Abhishek N. Kulkarni*, Andy Liu*, Jean-Raphael Gaglione, Daniel
Fried, and Ufuk Topcu
International Conference on Autonomous Agents and Multiagent Systems
(AAMAS), 2025
AutoPresent:
Designing Structured Visuals from Scratch
Jiaxin Ge*, Zora Zhiruo Wang*, Xuhui Zhou, Yi-Hao Peng, Sanjay
Subramanian, Qinyue Tan, Maarten Sap, Alane Suhr**, Daniel
Fried**, Graham Neubig**, and Trevor Darrell**
Conference on Computer Vision and Pattern Recognition (CVPR),
2025
CRScore:
Grounding Automated Evaluation of Code Review Comments in Code Claims
and Smells
Atharva Naik, Marcus Alenius, Daniel Fried, and Carolyn
Rose
North American Chapter of the Association for Computational
Linguistics (NAACL), 2025
CodeRAG-Bench:
Can Retrieval Augment Code Generation?
Zora Zhiruo Wang*, Akari Asai*, Xinyan Velocity Yu, Frank F. Xu, Yiqing
Xie, Graham Neubig, and Daniel Fried
Findings of NAACL, 2025
BigCodeBench:
Benchmarking Code Generation with Diverse Function Calls and Complex
Instructions
Terry Yue Zhuo et al. (33 authors from the BigCode project)
International Conference on Learning Representations (ICLR),
2025
Human-Aligned
Chess with a Bit of Search
Yiming Zhang, Athul Paul Jacob, Vivian Lai, Daniel Fried, and
Daphne Ippolito
International Conference on Learning Representations (ICLR),
2025
Repetition
Improves Language Model Embeddings
Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham
Neubig, and Aditi Raghunathan
International Conference on Learning Representations (ICLR),
2025
Dissecting
Adversarial Robustness of Multimodal LM Agents
Chen Henry Wu, Rishi Shah, Jing Yu Koh, Ruslan Salakhutdinov, Daniel
Fried, and Aditi Raghunathan
International Conference on Learning Representations (ICLR),
2025
Comparative
Knowledge Distillation
Alex Tianyi Xu*, Alex Wilf*, Paul Pu Liang, Alexander Obolenskiy,
Daniel Fried, and Louis-Philippe Morency
Winter Conference on Applications of Computer Vision (WACV),
2024
ECCO: Can We
Improve Model-Generated Code Efficiency Without Sacrificing Functional
Correctness?
Siddhant Waghjale*, Vishruth Veerendranath*, Zora Zhiruo Wang, and
Daniel Fried
Empirical Methods in Natural Language Processing (EMNLP),
2024
What Are Tools
Anyway? A Survey from the Language Model Perspective
Zora Zhiruo Wang, Zhoujun Cheng, Hao Zhu, Daniel Fried, and
Graham Neubig
Conference on Language Modeling (COLM), 2024
Human-Agent
Cooperation in Games under Incomplete Information through Natual
Language Communication
Shenghui Chen, Daniel Fried, and Ufuk Topcu
International Joint Conference on Artificial Intelligence
(IJCAI), 2024
Evaluating
Large Language Model Biases in Person-Steered
Generation
Andy Liu, Mona T. Diab, and Daniel Fried
Findings of ACL, 2024
Is the Pope
Catholic? Yes, the Pope is Catholic. Generative Evaluation of Intent
Resolution in LLMs
Akhila Yerukola, Saujas Vaduguru, Daniel Fried, and Maarten
Sap
Annual Meeting of the Association for Computational Linguistics
(ACL), 2024
VisualWebArena:
Evaluating Multimodal Agents on Realistic Visual Web
Tasks
Jing Yu Koh, Robert Lo*, Lawrence Jang*, Vikram Duvvur*, Ming Chong
Lim*, Po-Yu Huang*, Graham Neubig, Shuyan Zhou, Ruslan Salakhutdinov,
and Daniel Fried
Annual Meeting of the Association for Computational Linguistics
(ACL), 2024
TroVE:
Inducing Verifiable and Efficient Toolboxes for Solving Programmatic
Tasks
Zhiruo Wang, Graham Neubig, and Daniel Fried
International Conference on Machine Learning (ICML),
2024
Amortizing
Pragmatic Program Synthesis with Rankings
Yewen Pu, Saujas Vaduguru, Priyan Vaithilingam, Elena Glassman, and
Daniel Fried
International Conference on Machine Learning (ICML),
2024
Asking More
Informative Questions for Grounded Retrieval
Sedrick Keh, Justin T. Chiu, and Daniel Fried
Findings of NAACL, 2024
Generating
Pragmatic Examples to Train Neural Program
Synthesizers
Saujas Vaduguru, Daniel Fried, and Yewen Pu
International Conference on Learning Representations (ICLR),
2024
Sotopia:
Interactive Evaluation for Social Intelligence in Language
Agents
Xuhui Zhou*, Hao Zhu*, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang
Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham
Neubig, and Maarten Sap
International Conference on Learning Representations (ICLR),
2024
WebArena: A
Realistic Web Environment for Building Autonomous
Agents
Shuyan Zhou*, Frank Xu*, Hao Zhu**, Xuhui Zhou**, Robert Lo**, Abishek
Sridhar**, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon,
and Graham Neubig
International Conference on Learning Representations (ICLR),
2024
API-Assisted
Code Generation for Question Answering on Varied Table
Structures
Yihan Cao*, Shuyi Chen*, Ryan Liu*, Zhiruo Wang, and Daniel
Fried
Empirical Methods in Natural Language Processing (EMNLP),
2023
Symbolic
Planning and Code Generation for Grounded Dialogue
Justin Chiu, Wenting Zhao, Derek Chen, Saujas Vaduguru, Alexander Rush,
and Daniel Fried
Empirical Methods in Natural Language Processing (EMNLP),
2023
Pragmatics in
Language Grounding: Phenomena, Tasks, and Modeling
Approaches
Daniel Fried*, Nicholas Tomlin*, Jennifer Hu, Roma Patel, and
Aida Nematzadeh
Findings of EMNLP, 2023
Execution-Based
Evaluation for Open-Domain Code Generation
Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig
Findings of EMNLP, 2023
Data
Augmentation for Code Translation with Comparable Corpora and Multiple
References
Yiqing Xie, Atharva Naik, Daniel Fried, Carolyn Rose
Findings of EMNLP, 2023
AutoReply:
Detecting Nonsense in Dialogue Introspectively with Discriminative
Replies
Weiyan Shi, Emily Dinan, Adi Renduchintala, Daniel Fried, Athul
Paul Jacob, Zhou Yu, and Mike Lewis
Findings of EMNLP, 2023
Generating
Images with Multimodal Language Models
Jing Yu Koh, Daniel Fried, and Ruslan Salakhutdinov
Neural Information Processing Systems (NeurIPS), 2023
Pragmatic
Inference with a CLIP Listener for Contrastive
Captioning
Jiefu Ou, Benno Krojer, and Daniel Fried
Findings of ACL, 2023
Contrastive
Decoding: Open-ended Text Generation as Optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason
Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis
Annual Meeting of the Association for Computational Linguistics
(ACL), 2023
Grounding
Language Models to Images for Multimodal Inputs and
Outputs
Jing Yu Koh, Ruslan Salakhutdinov, and Daniel Fried
International Conference on Machine Learning (ICML),
2023
Coder Reviewer
Reranking for Code Generation
Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih,
Daniel Fried, and Sida I. Wang
International Conference on Machine Learning (ICML),
2023
DS-1000: A
Natural and Reliable Benchmark for Data Science Code
Generation
Yuhang Lai*, Chengxi Li*, Yiming Wang*, Tianyi Zhang*, Ruiqi Zhong*,
Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida I. Wang,
and Tao Yu
International Conference on Machine Learning (ICML),
2023
InCoder: A
Generative Model for Code Infilling and Synthesis
Daniel Fried*, Armen Aghajanyan*, Jessy Lin, Sida I. Wang, Eric
Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike
Lewis
International Conference on Learning Representations (ICLR),
2023
Natural
Language to Code Translation with Execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer,
and Sida I. Wang
Empirical Methods in Natural Language Processing (EMNLP),
2022
Neural
Theory-of-Mind? On the Limits of Social Intelligence in Large
LMs
Maarten Sap, Ronan Le Bras, Daniel Fried, and Yejin Choi
Empirical Methods in Natural Language Processing (EMNLP),
2022
G3:
Geolocation via Guidebook Grounding
Grace Luo*, Giscard Biamby*, Trevor Darrell, Daniel Fried, and
Anna Rohrbach
Findings of EMNLP, 2022
Inferring
Rewards from Language in Context
Jessy Lin, Daniel Fried, Dan Klein, and Anca Dragan
Annual Meeting of the Association for Computational Linguistics
(ACL), 2022
Reference-Centric Models
for Grounded Collaborative Dialogue
Daniel Fried, Justin Chiu, and Dan Klein
Empirical Methods in Natural Language Processing (EMNLP),
2021
Modular
Networks for Compositional Instruction Following
Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, and Trevor
Darrell
North American Chapter of the Association for Computational
Linguistics (NAACL), 2021
Learning to
Segment Actions from Observation and Narration
Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom, Chris Dyer,
Stephen Clark, Aida Nematzadeh
Annual Meeting of the Association for Computational Linguistics
(ACL), 2020
Cross-Domain
Generalization of Neural Constituency Parsers
Daniel Fried*, Nikita Kitaev*, and Dan Klein
Annual Meeting of the Association for Computational Linguistics
(ACL), 2019
Are You
Looking? Grounding to Multiple Modalities in Vision-and-Language
Navigation
Ronghang Hu, Daniel Fried, Anna Rohrbach, Dan Klein, Trevor
Darrell, and Kate Saenko
Annual Meeting of the Association for Computational Linguistics
(ACL), 2019
Pragmatically
Informative Text Generation
Sheng Shen, Daniel Fried, Jacob Andreas, and Dan Klein
North American Chapter of the Association for Computational
Linguistics (NAACL), 2019
Speaker-Follower Models
for Vision-and-Language Navigation
Daniel Fried*, Ronghang Hu*, Volkan Cirik*, Anna Rohrbach, Jacob
Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko,
Dan Klein**, and Trevor Darrell**
Neural Information Processing Systems (NeurIPS), 2018
Policy
Gradient as a Proxy for Dynamic Oracles in Constituency
Parsing
Daniel Fried and Dan Klein
Annual Meeting of the Association for Computational Linguistics
(ACL), 2018
Unified
Pragmatic Models for Generating and Following
Instructions
Daniel Fried, Jacob Andreas, and Dan Klein
North American Chapter of the Association for Computational
Linguistics (NAACL), 2018
Effective
Inference for Generative Neural Parsing
Mitchell Stern, Daniel Fried, and Dan Klein
Empirical Methods in Natural Language Processing (EMNLP),
2017
Improving
Neural Parsing by Disentangling Model Combination and Reranking
Effects
Daniel Fried*, Mitchell Stern*, and Dan Klein
Annual Meeting of the Association for Computational Linguistics
(ACL), 2017
Towards Using
Social Media to Identify Individuals at Risk for Preventable Chronic
Illness
Dane Bell, Daniel Fried, Luwen Huangfu, Mihai Surdeanu, and
Stephen Kobourov
Language Resources and Evaluation Conference (LREC),
2016
Low-Rank
Tensors for Verbs in Compositional Distributional
Semantics
Daniel Fried, Tamara Polajnar, and Stephen Clark
Annual Meeting of the Association for Computational Linguistics
(ACL), 2015
Analyzing
the Language of Food on Social Media
Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle,
and Dane Bell
International Conference on Big Data, 2014
Maps of
Computer Science
Daniel Fried and Stephen Kobourov
Pacific Visualization Symposium (PacificVis), 2014
Predicting
Parallelization of Sequential Programs Using Supervised
Learning
Daniel Fried, Zhen Li, Ali Jannesari, and Felix Wolf
International Conference on Machine Learning and Applications,
2013
A
Generative Probabilistic Framework for Learning Spatial
Language
Colin Dawson, Jeremy Wright, Antons Rebguns, Marco Valenzuela Escarcega,
Daniel Fried, and Paul Cohen
International Conference on Development and Learning, 2013.
Best Paper Award
Bayesian
Geometric Modeling of Indoor Scenes
Luca Del Pero, Joshua Bowdish, Daniel Fried, Bonnie Kermgard,
Emily Hartley, and Kobus Barnard
Conference on Computer Vision and Pattern Recognition (CVPR),
2012
SantaCoder:
Don’t Reach for the Stars
Loubna Ben Allal*, Raymond Li*, Denis Kocetkov*, et al. (41 authors from
the BigCode Project)
Deep Learning for Code Workshop, 2023. Best Paper
Award
Modeling
Perspective-Dependent Ambiguity in Grounded Collaborative
Dialogue
Justin Chiu, Wenting Zhao, Alexander M. Rush, and Daniel
Fried
Wordplay: When Language Meets Games Workshop, 2022
Interactive
Assignments for Teaching Structured Neural NLP
David Gaddy, Daniel Fried, Nikita Kitaev, Mitchell Stern, Rodolfo
Corona, John DeNero, and Dan Klein
Teaching NLP Workshop at NAACL, 2021
Challenges for Using Social Media for Early Detection of
Type II Diabetes Mellitus
Dane Bell, Daniel Fried, Luwen Huangfu, Mihai Surdeanu, and
Stephen Kobourov
International Workshop on Social Media World Sensors,
2016
Learning Low-Rank Tensors for Transitive
Verbs
Daniel Fried, Tamara Polajnar, and Stephen Clark
Advances in Distributional Semantics Workshop, 2015
Incorporating
both Distributional and Relational Semantics in Word
Representations
Daniel Fried and Kevin Duh
International Conference on Learning Representations (ICLR)
Workshop, 2015
Inducing
Programmatic Skills for Agentic Tasks
Zora Zhiruo Wang, Apurva Gandhi, Graham Neubig, Daniel
Fried
arXiv, 2025
RepoST:
Scalable Repository-Level Coding Environment Construction with Sandbox
Testing
Yiqing Xie, Alex Xie, Divyanshu Sheth, Pengfei Liu, Daniel Fried,
Carolyn Rose
arXiv, 2025
SWE-RL:
Advancing LLM Reasoning via Reinforcement Learning on Open Software
Evolution
Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux, Lingming
Zhang, Daniel Fried, Gabriel Synnaeve, Rishabh Singh, and Sida I.
Wang
arXiv, 2025
CodeBenchGen:
Creating Scalable Execution-based Code Generation
Benchmarks
Yiqing Xie, Alex Xie, Divyanshu Sheth, Pengfei Liu, Daniel Fried,
and Carolyn Rose
arXiv, 2024
Tree Search
for Language Model Agents
Jing Yu Koh, Stephen McAleer, Daniel Fried, and Ruslan
Salakhutdinov
arXiv, 2024
Agent Workflow
Memory
Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, and Graham
Neubig
arXiv, 2024
Improving Model
Factuality with Fine-grained Critique-based Evaluator
Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin
Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel
Fried, and Hejia Zhang
arXiv, 2024
*,**: equal contribution
PhD thesis advisor:
Andre He (with Sean Welleck)
Jing Yu (JY) Koh (with Ruslan Salakhutdinov)
Andy Liu (with Mona Diab)
Saujas Vaduguru
Zhiruo (Zora) Wang (with Graham Neubig)
MLT thesis advisor:
Alex Xie (with Matt Gormley and Vincent Hellendoorn)
PhD committee member:
Shuyan Zhou
Aman Madaan
Ta-Chung Chi
Nikitha Rao
Frank Xu
Ritam Dutt
Ruohong Zhang
So Yeon (Tiffany) Min
Anthony Sicilia (Northeastern)
Xing Han Lu (Mila/McGill)
Kush Jain
Brendon Boldt
Sanjay Subramanian (UC Berkeley)
Instructor: 11-891, Neural Code Generation, CMU, Spring 2024
Instructor: 11-877, Advanced Multimodal Machine Learning, CMU, Spring 2024
Instructor: 11-711, Advanced NLP, CMU, Fall 2023
Instructor: 11-777, Multimodal Machine Learning, CMU, 2023 – present
Instructor: CS188, Artificial Intelligence, UC Berkeley, Summer 2018
Teaching Assistant: CS188, Artificial Intelligence, UC Berkeley, Fall 2017
Teaching Assistant: CS245, Intro to Discrete Structures, University of Arizona, Spring 2012
Teaching Assistant: ISTA100, Great Ideas of the Information Age, University of Arizona, Fall 2011
Project Design: CS288, Natural Language Processing, UC Berkeley, Spring 2020
Planning and Inferring with LLMs for Grounded, Interactive Tasks. Forum for Artificial Intelligence, UT Austin. Fall 2024
Benchmarks and Tree Search for Multimodal LLM Agents. SpLU-RoboNLP Workshop, ACL 2024. Summer 2024
Interacting with LLMs for Grounded Tasks. NILLI Workshop, EMNLP 2023. Spring 2023
Interacting with (code) LLMs. MIT Neurosymbolic Reading Group. Spring 2023
InCoder, SantaCoder, and StarCoder: Findings from Training Open-Source Code LLMs. Bloomberg AI; GitHub Next. Spring 2023
Using Language Strategically in Context. CoRL Workshop on Strategic Multi-Agent Interactions, 2022; Johns Hopkins University, University of Pennsylvania, UT Austin. Spring 2023
Contextual Communication in Programming. LTI Future of Code Generation Seminar, 2022
Reasoning About Actions and Rewards in Language Interactions. MIT CPL, 2022. (with Jessy Lin)
Modularity in Grounded Interaction. ViGIL Workshop, NAACL 2021. (with Rudy Corona)
Learning Grounded Pragmatic Communication. University of Arizona, TTI-Chicago, University of Southern California, Purdue, Carnegie Mellon University, UC Irvine, Université de Montréal/Mila, Allen Institute for Artificial Intelligence, Facebook AI Research, Google Research, Stanford Cognition and Language Workshop. Spring 2021
Pragmatic Models for Generating and Following Grounded Instructions. Stanford NLP Seminar, University of Arizona, USC ISI. Fall 2018–Spring 2019
Workshop co-organizer: Workshop on Large Language Models for Agents, ICLR 2024
Workshop co-organizer: 3rd UnImplicit Workshop, EACL 2024
Workshop co-organizer: 2nd UnImplicit Workshop, NAACL 2022
Workshop co-organizer: 2nd Advances in Language and Vision Workshop, NAACL 2021
Workshop advisory board: Theory of Mind in Communicating Agents, ICML 2023
Outstanding reviewer awards: ACL 2018, NeurIPS 2019, ACL 2020, ACL 2021, ACL 2022
Senior area chair: ACL 2024
Area chair: EMNLP 2022–2024, ACL 2023
Ethics chair: NAACL 2024
Reviewing: TACL 2022–2024; ACL Rolling Review
2021–2023; ACL 2018–2022; EMNLP 2016–2021; NAACL 2019, 2021; AACL-IJCNLP
2020; EACL 2017, 2021; NeurIPS 2019–2023; ICML 2019, 2023; ICLR
2021–2023; AKBC 2021; COLING 2018; *SEM 2016–2018; NAACL-SRW 2016, 2018;
ACL-SRW 2020; IJCAI 2016; SpLU-RoboNLP 2019, 2021; NeuralGen 2019; ViGIL
2019, 2021; DeepLo 2019; ALVR 2020, 2021; LaReL 2020; Cooperative AI
2021; Meaning in Context 2021