@inproceedings{iyer-etal-2023-question,
title = "Question-Answer Sentence Graph for Joint Modeling Answer Selection",
author = "Iyer, Roshni and
Vu, Thuy and
Moschitti, Alessandro and
Sun, Yizhou",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.68/",
doi = "10.18653/v1/2023.eacl-main.68",
pages = "968--979",
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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%0 Conference Proceedings
%T Question-Answer Sentence Graph for Joint Modeling Answer Selection
%A Iyer, Roshni
%A Vu, Thuy
%A Moschitti, Alessandro
%A Sun, Yizhou
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F iyer-etal-2023-question
%X 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.
%R 10.18653/v1/2023.eacl-main.68
%U https://aclanthology.org/2023.eacl-main.68/
%U https://doi.org/10.18653/v1/2023.eacl-main.68
%P 968-979
Markdown (Informal)
[Question-Answer Sentence Graph for Joint Modeling Answer Selection](https://aclanthology.org/2023.eacl-main.68/) (Iyer et al., EACL 2023)
ACL