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        <title>Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs</title>
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        <namePart type="given">Tianqing</namePart>
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    <abstract>Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit contextunderlying that relationship. However, data scarcity makes it challenging for language models to learn to generate commonsense infer-ences for contexts and questions involving interactions between complex events. To address this demand, we present COM2 (COMplexCOMmonsense), a new dataset created by sampling multi-hop logical queries (e.g., the joint effect or cause of both event A and B, or theeffect of the effect of event C) from an existing commonsense knowledge graph (CSKG), and verbalizing them using handcrafted rules andlarge language models into multiple-choice and text generation questions. Our experiments show that language models trained on COM2 exhibit significant improve ments in complex reasoning ability, resulting in enhanced zero-shot performance in both in-domain and out-of-domain tasks for question answering and generative commonsense reasoning, without expensive human annotations</abstract>
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%0 Conference Proceedings
%T Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
%A Fang, Tianqing
%A Chen, Zeming
%A Song, Yangqiu
%A Bosselut, Antoine
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F fang-etal-2024-complex
%X Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit contextunderlying that relationship. However, data scarcity makes it challenging for language models to learn to generate commonsense infer-ences for contexts and questions involving interactions between complex events. To address this demand, we present COM2 (COMplexCOMmonsense), a new dataset created by sampling multi-hop logical queries (e.g., the joint effect or cause of both event A and B, or theeffect of the effect of event C) from an existing commonsense knowledge graph (CSKG), and verbalizing them using handcrafted rules andlarge language models into multiple-choice and text generation questions. Our experiments show that language models trained on COM2 exhibit significant improve ments in complex reasoning ability, resulting in enhanced zero-shot performance in both in-domain and out-of-domain tasks for question answering and generative commonsense reasoning, without expensive human annotations
%U https://aclanthology.org/2024.acl-long.613
%P 11365-11384

Markdown (Informal)

[Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs](https://aclanthology.org/2024.acl-long.613) (Fang et al., ACL 2024)

• Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs (Fang et al., ACL 2024)

ACL

• Tianqing Fang, Zeming Chen, Yangqiu Song, and Antoine Bosselut. 2024. Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11365–11384, Bangkok, Thailand. Association for Computational Linguistics.