Papers by Swarat Chaudhuri
Natural Language Deduction with Incomplete Information (2022.emnlp-main)
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| Challenge: | Existing systems for reasoning given incomplete information are inadequate . current approaches to reasoning are based on latent reasoning by large language models . |
| Approach: | They propose a system that generates a natural language "proof" by abductively inferring a premise from another premise and a conclusion. |
| Outcome: | The proposed system can handle the underspecified setting where not all premises are stated at the outset; additional assumptions need to be materialized to prove a claim. |
Flexible Generation of Natural Language Deductions (2021.emnlp-main)
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| Challenge: | Developing models that can make useful inferences from natural language premises has been a core goal in artificial intelligence since the field's early days. |
| Approach: | They propose a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision. |
| Outcome: | The proposed model is more accurate and flexible than baseline systems. |
Natural Language Deduction through Search over Statement Compositions (2022.findings-emnlp)
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| Challenge: | Existing methods focus on the end-to-end discriminative version of this task, but less work has treated the generative version of the task. |
| Approach: | They propose a system that decomposes the task into separate steps coordinated by a search procedure and produces a tree of intermediate conclusions that faithfully reflects the system’s reasoning process. |
| Outcome: | The proposed system proves true statements while rejecting false ones on the EntailmentBank dataset with a 17% absolute higher step validity than the end-to-end T5 model. |