Learning and Language

2023

  1. Leveraging Multiple Teachers for Test-Time Adaptation of Language-Guided Classifiers
    Kangda Wei, Sayan Ghosh, Rakesh R Menon, and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv], [code]

  2. MaNtLE: Model-agnostic Natural Language Explainer
    Rakesh R Menon, Kerem Zaman, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv], [code]

  3. Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
    Yiyuan Li, Rakesh R Menon, Sayan Ghosh, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv], [code]

  4. LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning
    Sayan Ghosh*, Rakesh R Menon*, and Shashank Srivastava
    Findings of Association for Computational Linguistics (ACL), 2023.
    [pdf], [arxiv], [code]

  5. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
    Big-Bench Collaboration
    Transactions of Machine Learning Research (TMLR), 2023.
    [openreview], [pdf], [arxiv], [code], [dataset]

2022

  1. What do Large Language Models Learn beyond Language?
    Avinash Madasu and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022.
    [pdf], [arxiv], [code]

  2. CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
    Rakesh R Menon*, Sayan Ghosh*, and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [arxiv], [code], [dataset]

2020

  1. Learning Web-based procedures by Reasoning over Explanations and Demonstrations in Context
    Shashank Srivastava, Oleksandr Polozov, Nebojsa Jojic, and Christopher Meek
    Proceedings of the Association of Computational Linguistics (ACL), 2020.
    [pdf], [dataset]

  2. An Agent for Learning New Natural Language Commands
    Amos Azaria, Shashank Srivastava, Jayant Krishnamurthy, Igor Labutov, and Tom Mitchell
    Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2020.
    [link]

2019

  1. Learning to Ask for Conversational Machine Learning
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019.
    [pdf]

2018

  1. LIA: A Natural Language Programmable Personal Assistant
    Igor Labutov, Shashank Srivastava, and Tom Mitchell
    Systems Demo, Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2018.
    [pdf]

  2. Zero-shot Learning of Classifiers from Natural Language Quantification
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of the Association of Computational Linguistics (ACL), 2018.
    [pdf]

2017

  1. Learning Classifiers from Declarative Language
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    NeurIPS Workshop on Learning from Limited Data, 2017.
    [pdf]

  2. Joint Concept Learning and Semantic Parsing from Natural Language Explanations
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2017.
    [pdf]

Datasets and Benchmarks

2023

  1. Identifying and Manipulating the Personality Traits of Language Models
    Graham Caron and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv]

  2. Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models
    Yiyuan Li, Rakesh R Menon, Sayan Ghosh, and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv], [code]

  3. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
    Big-Bench Collaboration
    Transactions of Machine Learning Research (TMLR), 2023.
    [openreview], [pdf], [arxiv], [code], [dataset]

2022

  1. Predicting Difficulty and Discrimination of Natural Language Questions
    Matthew Byrd and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [code], [dataset]

  2. ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding
    Sayan Ghosh and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [arxiv], [code], [dataset]

  3. CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations
    Rakesh R Menon*, Sayan Ghosh*, and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [arxiv], [code], [dataset]

2020

  1. Learning Web-based procedures by Reasoning over Explanations and Demonstrations in Context
    Shashank Srivastava, Oleksandr Polozov, Nebojsa Jojic, and Christopher Meek
    Proceedings of the Association of Computational Linguistics (ACL), 2020.
    [pdf], [dataset]

2018

  1. Where have I heard this story before? : Identifying Narrative Similarity in Movie Remakes
    Snigdha Chaturvedi, Shashank Srivastava, and Dan Roth
    Proceedings of the North Americal Chapter of Association of Computational Linguistics (NAACL), 2018.
    [pdf]

2017

  1. Joint Concept Learning and Semantic Parsing from Natural Language Explanations
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2017.
    [pdf]

  2. Parsing Natural Language Conversations with Contextual Cues
    Shashank Srivastava, Amos Azaria, and Tom Mitchell
    Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017.
    [pdf]

2016

  1. Inferring Interpersonal Relations in Narrative Summaries
    Shashank Srivastava, Snigdha Chaturvedi, and Tom Mitchell
    Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), 2016.
    [pdf]

Learning from Limited Labels

2022

  1. Compositional Generalization for Kinship Prediction through Data Augmentation
    Kangda Wei, Sayan Ghosh, and Shashank Srivastava
    Proceedings of the 4th Workshop of Narrative Understanding (WNU), 2022.
    [pdf], [code]

2021

  1. Improving and Simplifying Pattern Exploiting Training
    Derek Tam*, Rakesh R Menon*, Mohit Bansal, Shashank Srivastava, and Colin Raffel
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    [pdf], [arxiv], [code]

2020

  1. Learning Web-based procedures by Reasoning over Explanations and Demonstrations in Context
    Shashank Srivastava, Oleksandr Polozov, Nebojsa Jojic, and Christopher Meek
    Proceedings of the Association of Computational Linguistics (ACL), 2020.
    [pdf], [dataset]

  2. An Agent for Learning New Natural Language Commands
    Amos Azaria, Shashank Srivastava, Jayant Krishnamurthy, Igor Labutov, and Tom Mitchell
    Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), 2020.
    [link]

2019

  1. Learning to Ask for Conversational Machine Learning
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2019.
    [pdf]

2018

  1. Zero-shot Learning of Classifiers from Natural Language Quantification
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of the Association of Computational Linguistics (ACL), 2018.
    [pdf]

2017

  1. Learning Classifiers from Declarative Language
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    NeurIPS Workshop on Learning from Limited Data, 2017.
    [pdf]

  2. Joint Concept Learning and Semantic Parsing from Natural Language Explanations
    Shashank Srivastava, Igor Labutov, and Tom Mitchell
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2017.
    [pdf]

Neuro-symbolic Learning

2021

  1. Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning
    Sayan Ghosh and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    [pdf], [arxiv], [code]

2020

  1. Learning Web-based procedures by Reasoning over Explanations and Demonstrations in Context
    Shashank Srivastava, Oleksandr Polozov, Nebojsa Jojic, and Christopher Meek
    Proceedings of the Association of Computational Linguistics (ACL), 2020.
    [pdf], [dataset]

2017

  1. Parsing Natural Language Conversations with Contextual Cues
    Shashank Srivastava, Amos Azaria, and Tom Mitchell
    Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2017.
    [pdf]

Fairness and Social Applications

2023

  1. Identifying and Manipulating the Personality Traits of Language Models
    Graham Caron and Shashank Srivastava
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv]

2022

  1. ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding
    Sayan Ghosh and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [arxiv], [code], [dataset]

2021

  1. Does Social Pressure Drive Persuasion in Online Fora?
    Ayush Jain and Shashank Srivastava
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    [pdf]

  2. Adversarial Scrubbing of Demographic Information for Text Classification
    Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier B Oliva, Shashank Srivastava, and Snigdha Chaturvedi
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2021.
    [pdf], [arxiv], [code]

2018

  1. Where have I heard this story before? : Identifying Narrative Similarity in Movie Remakes
    Snigdha Chaturvedi, Shashank Srivastava, and Dan Roth
    Proceedings of the North Americal Chapter of Association of Computational Linguistics (NAACL), 2018.
    [pdf]

2016

  1. Inferring Interpersonal Relations in Narrative Summaries
    Shashank Srivastava, Snigdha Chaturvedi, and Tom Mitchell
    Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), 2016.
    [pdf]

  2. Modeling Evolving Relationships Between Characters in Literary Novel
    Snigdha Chaturvedi, Shashank Srivastava, Hal Daume III, and Chris Dyer
    Proceedings of the Association for the Advancement of Artificial Intelligence (AAAI), 2016.
    [pdf]

Active Learning

2023

  1. Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
    Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, and Dakuo Wang
    Findings of Empirical Methods in Natural Language Processing (EMNLP), 2023.
    [pdf], [arxiv]

Language Understanding, Reasoning, and Generation

2022

  1. Predicting Difficulty and Discrimination of Natural Language Questions
    Matthew Byrd and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [code], [dataset]

  2. ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding
    Sayan Ghosh and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2022.
    [pdf], [arxiv], [code], [dataset]

2021

  1. How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?
    Sayan Ghosh*, Zheng Qi*, Snigdha Chaturvedi, and Shashank Srivastava
    Proceedings of Association for Computational Linguistics (ACL), 2021.
    [pdf], [code]

2020

  1. PRover: Proof Generation for Interpretable Reasoning over Rules
    Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2020.
    [pdf], [arxiv], [code]

Syntax and Semantics

2018

  1. A Spatial Model for Extracting and Visualizing Latent Discourse Structure in Text
    Shashank Srivastava and Nebojsa Jojic
    Proceedings of the Association of Computational Linguistics (ACL), 2018.
    [pdf]

2016

  1. CMUML Micro-Reader System for KBP 2016 Cold Start Slot Filling, Event Nugget Detection, and Event Argument Linking
    Bishan Yang, Ndapandula Nakashole, Bryan Kisiel, Emmanouil A. Platanios, Abulhair Saparov, Shashank Srivastava, Derry Wijaya, and Tom Mitchell
    Proceedings of the Text Analysis Conference (TAC), 2016.
    [pdf]

2015

  1. CMU-ML System for KBP Cold Start Slot Filling
    Bryan Kisiel, Bill McDowell, Matt Gardner, Ndapandula Nakashole, Emmanouil A. Platanios, Abulhair Saparov, Shashank Srivastava, Derry Wijaya, and Tom Mitchell
    Proceedings of the Text Analysis Conference (TAC), 2015.
    [pdf]

2014

  1. Vector space semantics with frequency-driven motifs
    Shashank Srivastava and Eduard Hovy
    Proceedings of the Association of Computational Linguistics (ACL), 2014.
    [pdf]

2013

  1. A Walk-based Semantically Enriched Tree Kernel Over Distributed Word Representations
    Shashank Srivastava, Dirk Hovy, and Eduard Hovy
    Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2013.
    [pdf]

  2. A Structured Distributional Semantic Model for Event Co-reference
    Kartik Goyal*, Sujay Kumar Jauhar*, Huiying Li*, Mrinmaya Sachan*, Shashank Srivastava*, and Eduard Hovy
    Proceedings of the Association of Computational Linguistics (ACL), 2013.
    [pdf]

  3. A Structured Distributional Semantic Model : Integrating Structure with Semantics
    Kartik Goyal*, Sujay Kumar Jauhar*, Huiying Li*, Mrinmaya Sachan*, Shashank Srivastava*, and Eduard Hovy
    Workshop on Continuous Vector Space Models and their Compositionality, ACL, 2013.
    [pdf]

  4. Identifying Metaphorical Word Use with Tree Kernels
    Dirk Hovy, Shashank Srivastava, Sujay Kumar Jauhar, Mrinmaya Sachan, Kartik Goyal, Huiying Li, Whitney Sanders, and Eduard Hovy
    NAACL-HLT Meta4NLP Workshop, 2013.
    [pdf]

Miscellaneous

2014

  1. Spatial Compactness meets Topical Consistency: Jointly modeling link and content for community detection
    Mrinmaya Sachan, Avinava Dubey, Shashank Srivastava, Eric P Xing, and Eduard Hovy
    Proceedings of Web Search and Data Mining (WSDM), 2014.
    [pdf]

2012

  1. A Topical graph-kernel for Link Prediction in Labeled Graphs
    Snigdha Chaturvedi, Hal Daume III, Taesun Moon, and Shashank Srivastava
    ICML Workshop on Mining and Learning with Graphs (MLG), 2012.
    [pdf]