@inproceedings{21110c1cb31b42e997e453791c0bcd0b,
title = "I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning",
abstract = "CommonsenseQA is a task in which a correct answer is predicted through commonsense reasoning with pre-defined knowledge. Most previous works have aimed to improve the performance with distributed representation without considering the process of predicting the answer from the semantic representation of the question. To shed light upon the semantic interpretation of the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graph is pruned from a full integrated graph encompassing Abstract Meaning Representation (AMR) graph generated from input questions and an external commonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploited to interpret the reasoning path as well as to predict the correct answer on the CommonsenseQA task. This paper presents the manner in which the commonsense reasoning process can be interpreted with the relations and concepts provided by the ACP graph. Moreover, ACP-based models are shown to outperform the baselines.",
author = "Jungwoo Lim and Dongsuk Oh and Yoonna Jang and Kisu Yang and Heuiseok Lim",
note = "Publisher Copyright: {\textcopyright} 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.; 28th International Conference on Computational Linguistics, COLING 2020 ; Conference date: 08-12-2020 Through 13-12-2020",
year = "2020",
language = "English",
series = "COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "2459--2471",
editor = "Donia Scott and Nuria Bel and Chengqing Zong",
booktitle = "COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference",
address = "United States",
}