Studying Literary Characters and Character Networks. - Andrew Piper; Mark Algee-Hewitt; Koustuv Sinha; Derek Ruths; Hardik Vala (DH, 2017)

Ethical Challenges in Data-Driven Dialogue Systems. - Peter Henderson 002; Koustuv Sinha; Nicolas Angelard-Gontier; Nan Rosemary Ke; Genevieve Fried; Ryan Lowe; Joelle Pineau (AIES, 2018)

A Hierarchical Neural Attention-based Text Classifier. - Koustuv Sinha; Yue Dong 002; Jackie Chi Kit Cheung; Derek Ruths (EMNLP, 2018)

Adversarial Gain. - Peter Henderson 002; Koustuv Sinha; Nan Rosemary Ke; Joelle Pineau (CoRR, 2018)

The RLLChatbot: a solution to the ConvAI challenge. - Nicolas Gontier; Koustuv Sinha; Peter Henderson 002; Iulian Serban; Michael Noseworthy; Prasanna Parthasarathi; Joelle Pineau (CoRR, 2018)

Compositional Language Understanding with Text-based Relational Reasoning. - Koustuv Sinha; Shagun Sodhani; William L. Hamilton; Joelle Pineau (CoRR, 2018)

Evaluating systematicity in neural networks with natural language inference. - Emily Goodwin; Koustuv Sinha; Timothy J. O'Donnell (CogSci, 2019)

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text. - Koustuv Sinha; Shagun Sodhani; Jin Dong; Joelle Pineau; William L. Hamilton (EMNLP/IJCNLP (1), 2019)

Probing Linguistic Systematicity. - Emily Goodwin; Koustuv Sinha; Timothy J. O'Donnell (ACL, 2020)

Learning an Unreferenced Metric for Online Dialogue Evaluation. - Koustuv Sinha; Prasanna Parthasarathi; Jasmine Wang; Ryan Lowe; William L. Hamilton; Joelle Pineau (ACL, 2020)

Measuring Systematic Generalization in Neural Proof Generation with Transformers. - Nicolas Gontier; Koustuv Sinha; Siva Reddy; Christopher Pal (NeurIPS, 2020)

Evaluating Logical Generalization in Graph Neural Networks. - Koustuv Sinha; Shagun Sodhani; Joelle Pineau; William L. Hamilton (CoRR, 2020)

Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop. - Shagun Sodhani; Mayoore S. Jaiswal; Lauren Baker; Koustuv Sinha; Carl Shneider; Peter Henderson 002; Joel Lehman; Ryan Lowe (CoRR, 2020)

A Closer Look at Codistillation for Distributed Training. - Shagun Sodhani; Olivier Delalleau; Mahmoud Assran; Koustuv Sinha; Nicolas Ballas; Michael G. Rabbat (CoRR, 2020)

UnNatural Language Inference. - Koustuv Sinha; Prasanna Parthasarathi; Joelle Pineau; Adina Williams (ACL/IJCNLP (1), 2021)

Sometimes We Want Ungrammatical Translations. - Prasanna Parthasarathi; Koustuv Sinha; Joelle Pineau; Adina Williams (EMNLP (Findings), 2021)

Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little. - Koustuv Sinha; Robin Jia; Dieuwke Hupkes; Joelle Pineau; Adina Williams; Douwe Kiela (EMNLP (1), 2021)

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction. - Anuroop Sriram; Matthew J. Muckley; Koustuv Sinha; Farah Shamout; Joelle Pineau; Krzysztof J. Geras; Lea Azour; Yindalon Aphinyanaphongs; Nafissa Yakubova; William Moore (CoRR, 2021)

Sometimes We Want Translationese. - Prasanna Parthasarathi; Koustuv Sinha; Joelle Pineau; Adina Williams (CoRR, 2021)

Evaluating Gender Bias in Natural Language Inference. - Shanya Sharma; Manan Dey; Koustuv Sinha (CoRR, 2021)

Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program). - Joelle Pineau; Philippe Vincent-Lamarre; Koustuv Sinha; Vincent Larivière; Alina Beygelzimer; Florence d'Alché-Buc; Emily B. Fox; Hugo Larochelle (J. Mach. Learn. Res., 2021)

Towards Reproducible Machine Learning Research in Natural Language Processing. - Ana Lucic; Maurits J. R. Bleeker; Samarth Bhargav 001; Jessica Zosa Forde; Koustuv Sinha; Jesse Dodge; Sasha Luccioni; Robert Stojnic (ACL (tutorial), 2022)

Evaluating locality in NMT models. - Itay Itzhak; Koustuv Sinha; Brenden M. Lake; Adina Williams; Dieuwke Hupkes (CogSci, 2022)

How sensitive are translation systems to extra contexts? Mitigating gender bias in Neural Machine Translation models through relevant contexts. - Shanya Sharma; Manan Dey; Koustuv Sinha (EMNLP (Findings), 2022)

The Curious Case of Absolute Position Embeddings. - Koustuv Sinha; Amirhossein Kazemnejad; Siva Reddy; Joelle Pineau; Dieuwke Hupkes; Adina Williams (EMNLP (Findings), 2022)

Towards Reproducible Machine Learning Research in Information Retrieval. - Ana Lucic; Maurits J. R. Bleeker; Maarten de Rijke; Koustuv Sinha; Sami Jullien; Robert Stojnic (SIGIR, 2022)

State-of-the-art generalisation research in NLP: a taxonomy and review. - Dieuwke Hupkes; Mario Giulianelli; Verna Dankers; Mikel Artetxe; Yanai Elazar; Tiago Pimentel; Christos Christodoulopoulos 001; Karim Lasri; Naomi Saphra; Arabella Sinclair; Dennis Ulmer; Florian Schottmann; et al. (CoRR, 2022)

Language model acceptability judgements are not always robust to context. - Koustuv Sinha; Jon Gauthier; Aaron Mueller; Kanishka Misra; Keren Fuentes; Roger Levy; Adina Williams (ACL (1), 2023)

Robustness of Named-Entity Replacements for In-Context Learning. - Saeed Goodarzi; Nikhil Kagita; Dennis Minn; Shufan Wang; Roberto Dessì; Shubham Toshniwal; Adina Williams; Jack Lanchantin; Koustuv Sinha (EMNLP (Findings), 2023)

A taxonomy and review of generalization research in NLP. - Dieuwke Hupkes; Mario Giulianelli; Verna Dankers; Mikel Artetxe; Yanai Elazar; Tiago Pimentel; Christos E. Christodoulopoulos; Karim Lasri; Naomi Saphra; Arabella Sinclair; Dennis Ulmer; Florian Schottmann; et al. (Nat. Mac. Intell., 2023)

ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization. - David Wan; Koustuv Sinha; Srini Iyer 001; Asli Celikyilmaz; Mohit Bansal; Ramakanth Pasunuru (ACL (Findings), 2024)

The ART of LLM Refinement: Ask, Refine, and Trust. - Kumar Shridhar; Koustuv Sinha; Andrew Cohen; Tianlu Wang; Ping Yu; Ramakanth Pasunuru; Mrinmaya Sachan; Jason Weston; Asli Celikyilmaz (NAACL-HLT, 2024)

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning. - Shengbang Tong; David Fan; Jiachen Zhu 002; Yunyang Xiong; Xinlei Chen; Koustuv Sinha; Michael Rabbat; Yann LeCun; Saining Xie; Zhuang Liu 003 (CoRR, 2024)

Efficient Tool Use with Chain-of-Abstraction Reasoning. - Silin Gao; Jane Dwivedi-Yu; Ping Yu; Xiaoqing Ellen Tan; Ramakanth Pasunuru; Olga Golovneva; Koustuv Sinha; Asli Celikyilmaz; Antoine Bosselut; Tianlu Wang (COLING, 2025)

VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning. - Han Lin; Tushar Nagarajan; Nicolas Ballas; Mido Assran; Mojtaba Komeili; Mohit Bansal; Koustuv Sinha (ICLR, 2025)

Scaling Language-Free Visual Representation Learning. - David Fan; Shengbang Tong; Jiachen Zhu 002; Koustuv Sinha; Zhuang Liu 003; Xinlei Chen; Michael Rabbat; Nicolas Ballas; Yann LeCun; Amir Bar; Saining Xie (CoRR, 2025)

Multi-Modal Language Models as Text-to-Image Model Evaluators. - Jiahui Chen; Candace Ross; Reyhane Askari Hemmat; Koustuv Sinha; Melissa Hall; Michal Drozdzal; Adriana Romero-Soriano (CoRR, 2025)

CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models. - Aaron Foss; Chloe Evans; Sasha Mitts; Koustuv Sinha; Ammar Rizvi; Justine T. Kao (CoRR, 2025)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning. - Mido Assran; Adrien Bardes; David Fan; Quentin Garrido; Russell Howes; Mojtaba Komeili; Matthew J. Muckley; Ammar Rizvi; Claire Roberts; Koustuv Sinha; Artem Zholus; Sergio Arnaud; et al. (CoRR, 2025)

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs. - Benno Krojer; Mojtaba Komeili; Candace Ross; Quentin Garrido; Koustuv Sinha; Nicolas Ballas; Mahmoud Assran (CoRR, 2025)

Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track. - Rylan Schaeffer; Joshua Kazdan; Yegor Denisov-Blanch; Brando Miranda; Matthias Gerstgrasser; Susan Zhang; Andreas Haupt; Isha Gupta; Elyas Obbad; Jesse Dodge; Jessica Zosa Forde; Koustuv Sinha; et al. (CoRR, 2025)

Controlling Multimodal LLMs via Reward-guided Decoding. - Oscar Mañas; Pierluca D'Oro; Koustuv Sinha; Adriana Romero-Soriano; Michal Drozdzal; Aishwarya Agrawal (CoRR, 2025)