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        <title>COMET-M: Reasoning about Multiple Events in Complex Sentences</title>
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        <namePart type="given">Sahithya</namePart>
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    <name type="personal">
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    <abstract>Understanding the speaker’s intended meaning often involves drawing commonsense inferences to reason about what is not stated explicitly. In multi-event sentences, it requires understanding the relationships between events based on contextual knowledge. We propose COMET-M (Multi-Event), an event-centric commonsense model capable of generating commonsense inferences for a target event within a complex sentence. COMET-M builds upon COMET (Bosselut et al., 2019), which excels at generating event-centric inferences for simple sentences, but struggles with the complexity of multi-event sentences prevalent in natural text. To overcome this limitation, we curate a Multi-Event Inference (MEI) dataset of 35K human-written inferences. We train COMET-M on the human-written inferences and also create baselines using automatically labeled examples. Experimental results demonstrate the significant performance improvement of COMET-M over COMET in generating multi-event inferences. Moreover, COMET-M successfully produces distinct inferences for each target event, taking the complete context into consideration. COMET-M holds promise for downstream tasks involving natural text such as coreference resolution, dialogue, and story understanding.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.861</identifier>
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%0 Conference Proceedings
%T COMET-M: Reasoning about Multiple Events in Complex Sentences
%A Ravi, Sahithya
%A Ng, Raymond
%A Shwartz, Vered
%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 ravi-etal-2023-comet
%X Understanding the speaker’s intended meaning often involves drawing commonsense inferences to reason about what is not stated explicitly. In multi-event sentences, it requires understanding the relationships between events based on contextual knowledge. We propose COMET-M (Multi-Event), an event-centric commonsense model capable of generating commonsense inferences for a target event within a complex sentence. COMET-M builds upon COMET (Bosselut et al., 2019), which excels at generating event-centric inferences for simple sentences, but struggles with the complexity of multi-event sentences prevalent in natural text. To overcome this limitation, we curate a Multi-Event Inference (MEI) dataset of 35K human-written inferences. We train COMET-M on the human-written inferences and also create baselines using automatically labeled examples. Experimental results demonstrate the significant performance improvement of COMET-M over COMET in generating multi-event inferences. Moreover, COMET-M successfully produces distinct inferences for each target event, taking the complete context into consideration. COMET-M holds promise for downstream tasks involving natural text such as coreference resolution, dialogue, and story understanding.
%R 10.18653/v1/2023.findings-emnlp.861
%U https://aclanthology.org/2023.findings-emnlp.861
%U https://doi.org/10.18653/v1/2023.findings-emnlp.861
%P 12921-12937

Markdown (Informal)

[COMET-M: Reasoning about Multiple Events in Complex Sentences](https://aclanthology.org/2023.findings-emnlp.861) (Ravi et al., Findings 2023)

• COMET-M: Reasoning about Multiple Events in Complex Sentences (Ravi et al., Findings 2023)

ACL

• Sahithya Ravi, Raymond Ng, and Vered Shwartz. 2023. COMET-M: Reasoning about Multiple Events in Complex Sentences. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12921–12937, Singapore. Association for Computational Linguistics.