Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering

• URL: http://arxiv.org/abs/2305.04522v1
• Date: Mon, 8 May 2023 07:45:12 GMT
• Title: Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering
• Authors: Junru Lu, Gabriele Pergola, Lin Gui, Yulan He
• Abstract summary: We propose a strategy to incorporate event knowledge extracted from event trigger annotations via posterior regularization. In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets. We conduct experiments on two event-centric QA datasets, TORQUE and ESTER.
• Score: 32.03893317439898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a simple yet effective strategy to incorporate event knowledge extracted from event trigger annotations via posterior regularization to improve the event reasoning capability of mainstream question-answering (QA) models for event-centric QA. In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets, and subsequently use them to regularize the posterior answer output probabilities from the backbone pre-trained language models used in the QA setting. We explore two different posterior regularization strategies for extractive and generative QA separately. For extractive QA, the sentence-level event knowledge constraint is defined by assessing if a sentence contains an answer event or not, which is later used to modify the answer span extraction probability. For generative QA, the token-level event knowledge constraint is defined by comparing the generated token from the backbone language model with the answer event in order to introduce a reward or penalty term, which essentially adjusts the answer generative probability indirectly. We conduct experiments on two event-centric QA datasets, TORQUE and ESTER. The results show that our proposed approach can effectively inject event knowledge into existing pre-trained language models and achieves strong performance compared to existing QA models in answer evaluation. Code and models can be found: https://github.com/LuJunru/EventQAviaPR.

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