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.
Original language | English |
---|---|
Title of host publication | Findings of the Association for Computational Linguistics |
Subtitle of host publication | EMNLP 2023 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 12921-12937 |
Number of pages | 17 |
ISBN (Electronic) | 9798891760615 |
State | Published - 2023 |
Externally published | Yes |
Event | 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore Duration: 6 Dec 2023 → 10 Dec 2023 |
Publication series
Name | Findings of the Association for Computational Linguistics: EMNLP 2023 |
---|
Conference
Conference | 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 |
---|---|
Country/Territory | Singapore |
City | Singapore |
Period | 6/12/23 → 10/12/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.
Funding
This work was funded, in part, by the Vector Institute for AI, Canada CIFAR AI Chairs program, an NSERC discovery grant, and a research gift from AI2.
Funders | Funder number |
---|---|
Natural Sciences and Engineering Research Council of Canada | |
Vector Institute |