A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering

• URL: http://arxiv.org/abs/2303.10395v1
• Date: Sat, 18 Mar 2023 11:15:33 GMT
• Title: A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering
• Authors: Zhen Han, Yue Feng, and Mingming Sun
• Abstract summary: We propose a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. Experiments on two OpenCSR datasets show that the proposed model achieves great performance on benchmark OpenCSR datasets.
• Score: 21.61166185452341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, end-to-end trained models for multiple-choice commonsense question answering (QA) have delivered promising results. However, such question-answering systems cannot be directly applied in real-world scenarios where answer candidates are not provided. Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices. On the OpenCSR challenge set, many questions require implicit multi-hop reasoning and have a large decision space, reflecting the difficult nature of this task. Existing work on OpenCSR sorely focuses on improving the retrieval process, which extracts relevant factual sentences from a textual knowledge base, leaving the important and non-trivial reasoning task outside the scope. In this work, we extend the scope to include a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. The subgraph can be seen as a concise and compact graphical explanation of the prediction. Experiments on two OpenCSR datasets show that the proposed model achieves great performance on benchmark OpenCSR datasets.

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