Answer Generation for Retrieval-based Question Answering Systems
Chao-Chun Hsu, Eric Lind, Luca Soldaini, Alessandro Moschitti · 2021
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.