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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

February 1, 2023

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Authors

Shangwen Lv

Chinese Academy of Sciences


Daya Guo

Sun Yat-sen University


Jingjing Xu

Peking University


Duyu Tang

Microsoft Corporation


Nan Duan

Microsoft Corporation


Ming Gong

Microsoft Corporation


Linjun Shou

Microsoft Corporation


Daxin Jiang

Microsoft Corporation


Guihong Cao

Microsoft Corporation


Songlin Hu

Chinese Academy of Sciences


DOI:

10.1609/aaai.v34i05.6364


Abstract:

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, and answer questions based on the extracted evidence. Specifically, we extract evidence from both structured knowledge base (i.e. ConceptNet) and Wikipedia plain texts. We construct graphs for both sources to obtain the relational structures of evidence. Based on these graphs, we propose a graph-based approach consisting of a graph-based contextual word representation learning module and a graph-based inference module. The first module utilizes graph structural information to re-define the distance between words for learning better contextual word representations. The second module adopts graph convolutional network to encode neighbor information into the representations of nodes, and aggregates evidence with graph attention mechanism for predicting the final answer. Experimental results on CommonsenseQA dataset illustrate that our graph-based approach over both knowledge sources brings improvement over strong baselines. Our approach achieves the state-of-the-art accuracy (75.3%) on the CommonsenseQA dataset.

Topics: AAAI

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Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering Proceedings of the AAAI Conference on Artificial Intelligence (2020) 8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering AAAI 2020, 8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu (2020). Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence, 8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu. Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu. 2020. Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. "Proceedings of the AAAI Conference on Artificial Intelligence". 8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu. (2020) "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering", Proceedings of the AAAI Conference on Artificial Intelligence, p.8449-8456

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu, "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering", AAAI, p.8449-8456, 2020.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu. "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu. "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 8449-8456.

Shangwen Lv||Daya Guo||Jingjing Xu||Duyu Tang||Nan Duan||Ming Gong||Linjun Shou||Daxin Jiang||Guihong Cao||Songlin Hu. Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. AAAI[Internet]. 2020[cited 2023]; 8449-8456.


ISSN: 2374-3468


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