Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning

• URL: http://arxiv.org/abs/2205.01376v1
• Date: Tue, 3 May 2022 08:53:55 GMT
• Title: Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
• Authors: Oscar Sainz, Itziar Gonzalez-Dios, Oier Lopez de Lacalle, Bonan Min and Eneko Agirre
• Abstract summary: Recent work has shown that NLP tasks can be recasted as Textual Entailment tasks using verbalizations. We show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20%.
• Score: 22.531385318852426
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent work has shown that NLP tasks such as Relation Extraction (RE) can be recasted as Textual Entailment tasks using verbalizations, with strong performance in zero-shot and few-shot settings thanks to pre-trained entailment models. The fact that relations in current RE datasets are easily verbalized casts doubts on whether entailment would be effective in more complex tasks. In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training. More importantly, we show that recasting EAE as entailment alleviates the dependency on schemas, which has been a road-block for transferring annotations between domains. Thanks to the entailment, the multi-source transfer between ACE and WikiEvents further reduces annotation down to 10% and 5% (respectively) of the full training without transfer. Our analysis shows that the key to good results is the use of several entailment datasets to pre-train the entailment model. Similar to previous approaches, our method requires a small amount of effort for manual verbalization: only less than 15 minutes per event argument type is needed, and comparable results can be achieved with users with different level of expertise.

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