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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