Professional

CV: Link to CV

Publications: Summarization/NLG, Narrative understanding/Generation, SocialNLP, General NLP Topics, Other Topics

Summarization/NL Generation:

1. H Li, S Chaturvedi, ‘Rationale-based Opinion Summarization’, NAACL 2024

2. S Basu Roy Chowdhury, N Monath, K Avinava Dubey, M Zaheer, A McCallum, A Ahmed, S Chaturvedi, ‘Incremental Extractive Opinion Summarization Using Cover Trees’, TMLR 2024

3. S Basu Roy Chowdhury, N Monath, K Avinava Dubey, A Ahmed, S Chaturvedi, ‘Unsupervised Opinion Summarization Using Approximate Geodesics’, EMNLP-Findings 2023 [pdf]

4. H Li, S Basu Roy Chowdhury, S Chaturvedi, 'Aspect-aware Unsupervised Extractive Opinion Summarization’, Findings of ACL 2023 [pdf] [code]

5. C Zhao, F Brahman, K Song, W Yao, D Yu, S Chaturvedi, 'NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization', Findings of EMNLP 2022 [pdf][data and website]

6. S Basu Roy Chowdhury, C Zhao, S Chaturvedi, 'Unsupervised Extractive Opinion Summarization Using Sparse Coding', ACL 2022 [pdf]

7. C Zhao*, T Huang*, S Basu Roy Chowdhury, M K Chandrasekaran, K McKeown, S Chaturvedi, `Read Top News First: A Document Reordering Approach for Multi-document News Summarization', Findings of ACL 2022 [pdf]

8. C Zhao, S Chaturvedi, 'Weakly-supervised Opinion Summarization By Leveraging External Information', AAAI  2020 [pdf] [code] [poster]

NL Generation:

9. F Brahman, B Peng, M Galley, S Rao, B Dolan, S Chaturvedi, J Gao, 'Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation', Findings of EMNLP 2022 [pdf]

10. C Zhao, F Brahman, T Huang, S Chaturvedi, `Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach', Findings of NAACL 2022 [pdf]

11. S Ghosh, Z Qi, S Chaturvedi, S Srivastava, 'How Helpful is Inverse Reinforcement Learning for Table-to-Text Generation?', ACL 2021 [pdf]

12. C Zhao, M Walker, S Chaturvedi, 'Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation', ACL 2020. [pdf] [code] [lay summary] [poster]

Narrative Understanding/Generation:

1. A Brei, C Zhao, S Chaturvedi, ‘Returning to the Start: Generating Narratives with Related Endpoints', NAACL 2024

2. C Zhao, A Rao Vijjini, S Chaturvedi , 'PARROT: Zero-Shot Narrative Reading Comprehension via Parallel Reading’, EMNLP-Findings 2023 [pdf]

3. T Huang, E Qasemi, B Li, H Wang, F Brahman, M Chen, S Chaturvedi, ‘Affective and Dynamic Beam Search for Story Generation’, EMNLP-Findings 2023 [pdf]

4. A Rao Vijjini, F Brahman, S Chaturvedi, 'Towards Inter-character Relationship-driven Story Generation', EMNLP 2022 [arxiv pdf] [code]

5. C Zhao, F Brahman, K Song, W Yao, D Yu, S Chaturvedi, 'NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization', Findings of EMNLP 2022 [pdf][data and website]

6. S Basu Roy Chowdhury*, F Brahman*, S Chaturvedi, 'Is Everything in Order? A Simple Way to Order Sentences', EMNLP 2021 [pdf] [poster]

7. T Huang, F Brahman, V Shwartz, S Chaturvedi, 'Uncovering Implicit Gender Bias in Narratives through Commonsense Inference', Findings of EMNLP 2021 [pdf]

8. F Brahman, M Huang, O Tafjord, C Zhao, M Sachan, S Chaturvedi, '"Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding', Findings of EMNLP 2021 [pdf] [website]

9. F Brahman, S Chaturvedi, 'Modeling Protagonist Emotions for Emotion-Aware Storytelling', EMNLP 2020 [pdf][code][slides]

10. F Brahman, A Petrusca, S Chaturvedi, 'Cue Me In: Content-Inducing Approaches to Interactive Story Generation', AACL 2020 [pdf][slides]

11. S Chaturvedi, S Srivastava, D Roth, 'Where have I heard This Story Before?: Identifying Narrative Similarity in Movie Summaries', NAACL 2018 [pdf][data][poster]

12. S Chaturvedi, M Iyyer, H Daume III, 'Unsupervised Learning of Evolving Relationships Between Literary Characters', AAAI 2017  [pdf] [supplementary material] [data]

13. S Chaturvedi, H Peng, D Roth, 'Story Comprehension for Predicting What Happens Next', EMNLP 2017 [pdf][supplementary material][code (incl. data)][slides]

14. H Peng, S Chaturvedi, D Roth, 'A Joint Model for Semantic Sequences: Frames, Entities, Sentiments', CoNLL 2017 [pdf]

15. M Iyyer, A Guha, S Chaturvedi, J Boyd-Graber, H Daume III, 'Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships', NAACL 2016 (Best Paper Award) (PRESS: AEON) [pdf] [bibtex] 

16. S Chaturvedi, S Srivastava, H Daume III and C Dyer, ‘Modeling Evolving Relationships between Characters in Literary Novels’, AAAI 2016 [pdf] [Frames polarity lexicon] [data]

17. S Srivastava, S Chaturvedi, T Mitchell, ‘Inferring Interpersonal Relations in Narrative Summaries’, AAAI 2016 PRESS: New Scientist  [pdf] [data] 

18. S Chaturvedi, D Goldwasser and H Daume III, 'Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text', AAAI 2016 [pdf] [conformity lexicon] [data]

Social NLP:

Fairness in NLP

1. S Basu Roy Chowdhury, N Monath, A Beirami, R Kidambi, K Avinava Dubey, A Ahmed, S Chaturvedi, ‘Enhancing Group Fairness in Online Settings Using Oblique Decision Forests’, ICLR 2024

2. S Basu Roy Chowdhury, N Monath, K Avinava Dubey, A Ahmed, S Chaturvedi, ‘Robust Concept Erasure via Kernelized Rate-Distortion Maximization’, NeurIPS 2023 [arxiv pdf] [poster]

3. S Basu Roy Chowdhury, S Chaturvedi, 'Sustaining Fairness via Incremental Learning', AAAI 2023 [arxiv pdf]

4. S Basu Roy Chowdhury, S Chaturvedi, 'Learning Fair Representations via Rate-Distortion Maximization', TACL 2022 [pdf] [code]

5. S Basu Roy Chowdhury, S Ghosh, Y Li, J Oliva, S Srivastava, S Chaturvedi, 'Adversarial Scrubbing of Demographic Information for Text Classification', EMNLP 2021 [pdf] [poster]

6. T Huang, F Brahman, V Shwartz, S Chaturvedi, 'Uncovering Implicit Gender Bias in Narratives through Commonsense Inference', Findings of EMNLP 2021 [pdf]

NLP for Mental Health

1. A Rinaldi, J Fox-Tree, S Chaturvedi, 'Predicting Depression in Screening Interviews from Latent Categorization of Interview Prompts', ACL 2020. [pdf] [code] [lay summary]

NLP for Education

1. V Kumaran, J Rowe, B Mott, S Chaturvedi, J Lester, ‘Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers’, Findings of ACL 2023

2. G Giri, R M Scott, S Chaturvedi, `Effects of Remote Learning on Academic Performance of High School Students', The WiNLP Workshop, NAACL 2022.

3. F Brahman, N Varghese, S Bhat, S Chaturvedi, ‘Effective Forum Curation via Multi-task Learning’, International Conference on Educational Data Mining (EDM) 2020. [pdf]

4. N Norouzi, S Chaturvedi, M Rutledge, 'Lessons Learned from Teaching Machine Learning and Natural Language Processing to High School Students', AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI), 2020 [pdf]

5. Z Zeng, S Chaturvedi, S Bhat, D Roth, 'DiAd: Domain Adaptation for Learning at Scale', International Conference on Learning Analytics and Knowledge (LAK) 2019 [pdf]

6. Z Zeng, S Chaturvedi, S Bhat, 'Learner Affect Through the Looking Glass: Characterization and Detection of Confusion in Online Courses', International Conference on Educational Data Mining (EDM) 2017 [pdf]

7. S Chaturvedi, D Goldwasser and H Daume III, `Predicting Instructor Intervention in MOOC Forums', ACL, 2014 [pdf] [bibtex][poster]

Misc NLP papers:

1. A Rao Vijjini, H Deilamsalehy, F Dernoncourt, S Chaturvedi, 'Curricular Next Conversation Prediction Pretraining for Transcript Segmentation’, Findings of EACL 2023 [pdf]

2. Y Li, T Che, Y Wang, Z Jiang, C Xiong, S Chaturvedi, 'SPE: Symmetrical Prompt Enhancement for Fact Probing', EMNLP 2022 [pdf]

3. S Basu Roy Chowdhury, S Chaturvedi, ‘Does Commonsense help in detecting Sarcasm?’, Workshop on Insights from Negative Results in NLP, EMNLP 2021 [pdf]

4. S Mayhew, S Chaturvedi, C Tsai, D Roth, 'Named Entity Recognition with Partially Annotated Training Data', CoNLL 2019 [pdf] [code]

5. D Khashabi, S Chaturvedi, M Roth, S Upadhyay, D Roth, 'Looking beyond the surface: A challenge set for reading comprehension over multiple sentences', NAACL 2018 [pdf] [supp material] [data]

6. S Chaturvedi, V Castelli, R Nallapati, H Raghavan, R Florian, 'Joint Question Clustering and Relevance Prediction for Open Domain Non-factoid Question Answering', WWW 2014 [slides] [pdf] [bibtex]

7. K McKeown, H Daumé III, S Chaturvedi, J Paparrizos, K Thadani, P Barrio, O Biran, S Bothe, M Collins, K Fleischmann, L Gravano, R Jha, B King, K McInerney, T Moon, D O'Seaghdha, D Radev, C Templeton and S Teufel, 'Predicting the impact of scientific concepts using full text features', In Journal of the American Society for Information Science and Technology, 2015. Accepted July 10, 2015. Published in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/asi.23612 [pdf] (Nominated for the "ISSI Paper of the Year Award")

8. S Chaturvedi, H Daume III, T Moon, 'Discriminatively Enhanced Topic Models', ICDM 2013 [slides][pdf]

9. S Chaturvedi, H Daume III, T Moon, S Srivastava, 'A Topical Graph Kernel for Link Prediction in Labeled Graphs', ICML workshop on Mining and Learning with Graphs (MLG), Edinburgh, Scotland, July 1, 2012 [pdf][poster]

Other topics:

1. K Choromanski, A Sehanobish, H Lin, Y Zhao, E Berger, T Parshakova, A Pan, D Watkins, T Zhang, V Likhosherstov, S Basu Roy Chowdhury, K Avinava Dubey, D Jain, T Sarlos, S Chaturvedi, A Weller, `Efficient Graph Field Integrators Meet Point Clouds', ICML 2023

2. S Chaturvedi, C Dunne, Z Ashktorab, R Zachariah and B Shneiderman, 'Group-in-a-box Meta-Layouts for Topological Clusters and Attribute Based Groups: Space Efficient Visualizations of Network Communities and Their Ties', Computer Graphics Forum, Volume 33, Issue 8, pages 52–68, December 2014, issn:1467-8659. Published online (June 2, 2014) DOI: 10.1111/cgf.12400 [pdf]

3. S Chaturvedi, KH Prasad, TA Faruquie, LV Subramaniam, R Krishnapuram, 'Automating Pattern Discovery for Rule Based Data Standardization Systems', ICDE, 2013 [pdf]

4. KH Prasad, S Chaturvedi, TA Faruquie, LV Subramaniam, MK Mohania, 'Automated Selection of Blocking Columns for Record Linkage', IEEE International Conference on Service Operations and Logistics, and Informatics, Suzhou, China, July 8-10, 2012 [pdf]

5. KH Prasad, S Chaturvedi, TA Faruquie, LV Subramaniam, MK Mohania, 'Managing Data Quality by Identifying the Noisiest Data Samples', IEEE International Conference on Service Operations and Logistics, and Informatics, Suzhou, China, July 8-10, 2012 [pdf]

6. S Chaturvedi, TA Faruquie, LV Subramaniam, KH Prasad, G Venkatachaliah and S Padmanabhan, 'Choosing Optimal Training Data for Rule-based Data Cleansing Models', Service Research & Innovation Institute Global Conference (SRII), San Jose, California, USA, Mar 30-Apr 2, 2011 [pdf] [abstract]

7. KH Prasad , TA Faruquie, S Joshi, S Chaturvedi, LV Subramaniam, MK Mohania, 'Data Cleansing Techniques for Large Enterprise Datasets', Service Research & Innovation Institute Global Conference (SRII), San Jose, California, USA, March 30-April 2, 2011 [pdf]

8. S Chaturvedi, TA Faruquie, LV Subramaniam and MK Mohania, ‘Estimating Accuracy of Text Classifiers on Large Unlabeled Datasets’, in Proceedings of 19th ACM International Conference on Information and Knowledge Management (CIKM’10), Toronto, Ontario, Canada, October 26-30, 2010 [pdf] [abstract]

PhD dissertation: "Structured Approaches to Exploring Interpersonal Relationships in Natural Language Text" [compacted pdf]

PhD Proposal: [pdf][talk announcement]

Articles and Published Disclosure:

• 1. Course Project report: 'Understanding the Natural Language Processing Community'

  2. ‘Effort Estimation for Data Cleansing Jobs’, S Chaturvedi, MK Mohania, TA Faruquie and LV Subramaniam (Published in IP.com)

* Link to all the pdf documents listed on this page.