Question-Answer Sentence Graph for Joint Modeling Answer Selection

• URL: http://arxiv.org/abs/2203.03549v2
• Date: Sun, 23 Apr 2023 04:01:56 GMT
• Title: Question-Answer Sentence Graph for Joint Modeling Answer Selection
• Authors: Roshni G. Iyer, Thuy Vu, Alessandro Moschitti, Yizhou Sun
• Abstract summary: We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs. Online inference is then performed to solve the AS2 task on unseen queries.
• Score: 122.29142965960138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per question in an unsupervised manner, and integrates with Graph Neural Networks. Graph nodes are question sentence to answer sentence pairs. We train and integrate state-of-the-art (SOTA) models for computing scores between question-question, question-answer, and answer-answer pairs, and use thresholding on relevance scores for creating graph edges. Online inference is then performed to solve the AS2 task on unseen queries. Experiments on two well-known academic benchmarks and a real-world dataset show that our approach consistently outperforms SOTA QA baseline models.

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