@inproceedings{ling-etal-2023-open,
title = "Open-ended Commonsense Reasoning with Unrestricted Answer Candidates",
author = "Ling, Chen and
Zhang, Xuchao and
Zhao, Xujiang and
Liu, Yanchi and
Cheng, Wei and
Oishi, Mika and
Osaki, Takao and
Matsuda, Katsushi and
Chen, Haifeng and
Zhao, Liang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.540",
doi = "10.18653/v1/2023.findings-emnlp.540",
pages = "8035--8047",
abstract = "Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.",
}
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<abstract>Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.</abstract>
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%0 Conference Proceedings
%T Open-ended Commonsense Reasoning with Unrestricted Answer Candidates
%A Ling, Chen
%A Zhang, Xuchao
%A Zhao, Xujiang
%A Liu, Yanchi
%A Cheng, Wei
%A Oishi, Mika
%A Osaki, Takao
%A Matsuda, Katsushi
%A Chen, Haifeng
%A Zhao, Liang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ling-etal-2023-open
%X Open-ended Commonsense Reasoning is defined as solving a commonsense question without providing 1) a short list of answer candidates and 2) a pre-defined answer scope. Conventional ways of formulating the commonsense question into a question-answering form or utilizing external knowledge to learn retrieval-based methods are less applicable in the open-ended setting due to an inherent challenge. Without pre-defining an answer scope or a few candidates, open-ended commonsense reasoning entails predicting answers by searching over an extremely large searching space. Moreover, most questions require implicit multi-hop reasoning, which presents even more challenges to our problem. In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision. The reasoning paths can help to identify the most precise answer to the commonsense question. We conduct experiments on two commonsense benchmark datasets. Compared to other approaches, our proposed method achieves better performance both quantitatively and qualitatively.
%R 10.18653/v1/2023.findings-emnlp.540
%U https://aclanthology.org/2023.findings-emnlp.540
%U https://doi.org/10.18653/v1/2023.findings-emnlp.540
%P 8035-8047
Markdown (Informal)
[Open-ended Commonsense Reasoning with Unrestricted Answer Candidates](https://aclanthology.org/2023.findings-emnlp.540) (Ling et al., Findings 2023)
ACL
- Chen Ling, Xuchao Zhang, Xujiang Zhao, Yanchi Liu, Wei Cheng, Mika Oishi, Takao Osaki, Katsushi Matsuda, Haifeng Chen, and Liang Zhao. 2023. Open-ended Commonsense Reasoning with Unrestricted Answer Candidates. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 8035–8047, Singapore. Association for Computational Linguistics.