Papers with discrete reasoning
Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning (2022.acl-long)
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| Challenge: | Existing NDR models suffer from large performance drop on hypothetical questions, e.g., “what the annualized rate of return would be if the revenue in 2020 was doubled”. |
| Approach: | They propose a learning to imagine module which can be seamlessly incorporated into NDR models to perform the imagination of unseen counterfactual. |
| Outcome: | The proposed model can perform the imagination of unseen counterfactuals on hypothetical questions. |
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions (2020.emnlp-main)
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| Challenge: | Existing reading comprehension tasks focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system’s performance at identifying a potential lack of sufficient information and locating sources for that information. |
| Approach: | They propose to use a dataset with 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. |
| Outcome: | The proposed model achieves 31.1% F1 on the reading comprehension task, while estimated human performance is 88.4%. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Doc2SoarGraph: Discrete Reasoning over Visually-Rich Table-Text Documents via Semantic-Oriented Hierarchical Graphs (2024.lrec-main)
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| Challenge: | Existing work on document visual question answering fails to capture the differences and correlations between elements of a document and associated questions. |
| Approach: | They propose a document-visual question-answering challenge that exploits element-level semantics and employs hierarchical Graph structures to capture differences and correlations between elements. |
| Outcome: | The proposed model surpasses the state-of-the-art method and large language model in terms of Exact Match (EM) metric, demonstrating exceptional effectiveness. |
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation (2022.emnlp-main)
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| Challenge: | Existing methods for question answering using knowledge resources are mixed-of-experts and semantic parsing-based. |
| Approach: | They propose a semantic-parsing-based approach to perform Unified discrete Reasoning over heterogeneous knowledge resources as Program Generation. |
| Outcome: | The proposed approach improves interpretability and scalability over table and text . it achieves promising performance on the TAT-QA dataset without annotation . |