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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Performance Prediction for Non-Factoid Question Answering
Estimating the quality of a result list, often referred to as query performance prediction (QPP), is a challenging and important task in information retrieval. It can be used as feedback to users, search engines, and system administrators. Although predicting the performance of retrieval models has been extensively studied for the ad-hoc retrieval task, the effectiveness of performance prediction methods for question answering (QA) systems is relatively unstudied. The short length of answers, the dominance of neural models in QA, and the re-ranking nature of most QA systems make performance prediction for QA a unique, important, and technically interesting task. In this paper, we introduce and motivate the task of performance prediction for non-factoid question answering and propose a neural performance predictor for this task. Our experiments on two recent datasets demonstrate that the proposed model outperforms competitive baselines in all settings.
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- Award ID(s):
- 1715095
- PAR ID:
- 10143771
- Date Published:
- Journal Name:
- Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval - ICTIR '19
- Page Range / eLocation ID:
- 55 to 58
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3341981.3344249
