Papers by Daniel Deutch
Weakly Supervised Text-to-SQL Parsing through Question Decomposition (2022.findings-naacl)
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| Challenge: | Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. |
| Approach: | They propose a weak supervision approach for training text-to-SQL parsers by using a question meaning representation called QDMR to synthesize SQL queries from annotated NL-SqL data. |
| Outcome: | The proposed model performs competitively with those trained on annotated NL-SQL data. |
Break It Down: A Question Understanding Benchmark (2020.tacl-1)
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| Challenge: | Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. |
| Approach: | They introduce a Question Decomposition Meaning Representation (QDMR) for questions . they demonstrate that QDMRs can be annotated at scale using a hotpotQA dataset . |
| Outcome: | The proposed model outperforms several natural baselines in the open-domain question answering hotpotQA dataset and can be deterministically converted to a pseudo-SQL formal language. |
Answering Questions by Meta-Reasoning over Multiple Chains of Thought (2023.emnlp-main)
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| Challenge: | Modern systems for multi-hop question answering (QA) break questions into a sequence of reasoning steps, termed chain-of-thought (CoT) Often, multiple chains are sampled and aggregated, but the intermediate steps themselves are discarded. |
| Approach: | They propose a method which prompts large language models to meta-reason over multiple chains of thought rather than aggregate their answers. |
| Outcome: | The proposed approach outperforms baselines on 7 multi-hop QA datasets. |