@inproceedings{han-etal-2023-graph,
title = "A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering",
author = "Han, Zhen and
Feng, Yue and
Sun, Mingming",
editor = "Surdeanu, Mihai and
Riloff, Ellen and
Chiticariu, Laura and
Frietag, Dayne and
Hahn-Powell, Gus and
Morrison, Clayton T. and
Noriega-Atala, Enrique and
Sharp, Rebecca and
Valenzuela-Escarcega, Marco",
booktitle = "Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.pandl-1.3",
doi = "10.18653/v1/2023.pandl-1.3",
pages = "20--24",
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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<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.</abstract>
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%0 Conference Proceedings
%T A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering
%A Han, Zhen
%A Feng, Yue
%A Sun, Mingming
%Y Surdeanu, Mihai
%Y Riloff, Ellen
%Y Chiticariu, Laura
%Y Frietag, Dayne
%Y Hahn-Powell, Gus
%Y Morrison, Clayton T.
%Y Noriega-Atala, Enrique
%Y Sharp, Rebecca
%Y Valenzuela-Escarcega, Marco
%S Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F han-etal-2023-graph
%X 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.
%R 10.18653/v1/2023.pandl-1.3
%U https://aclanthology.org/2023.pandl-1.3
%U https://doi.org/10.18653/v1/2023.pandl-1.3
%P 20-24
Markdown (Informal)
[A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering](https://aclanthology.org/2023.pandl-1.3) (Han et al., PANDL-WS 2023)
ACL