Answer Generation for Retrieval-based Question Answering Systems

• URL: http://arxiv.org/abs/2106.00955v1
• Date: Wed, 2 Jun 2021 05:45:49 GMT
• Title: Answer Generation for Retrieval-based Question Answering Systems
• Authors: Chao-Chun Hsu, Eric Lind, Luca Soldaini, Alessandro Moschitti
• Abstract summary: We train a sequence to sequence transformer model to generate an answer from a candidate set. Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.
• Score: 80.28727681633096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based systems, have achieved impressive results. While generally effective, these models fail to provide a satisfying answer when all retrieved candidates are of poor quality, even if they contain correct information. In AS2, models are trained to select the best answer sentence among a set of candidates retrieved for a given question. In this work, we propose to generate answers from a set of AS2 top candidates. Rather than selecting the best candidate, we train a sequence to sequence transformer model to generate an answer from a candidate set. Our tests on three English AS2 datasets show improvement up to 32 absolute points in accuracy over the state of the art.

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