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Answer sentence selection (AS2) modeling requires annotated data, i.e.,hand-labeled question-answer pairs. We present a strategy to collect weaklysupervised answers for a question based on its reference to improve AS2modeling. Specifically, we introduce Reference-based Weak Supervision (RWS), afully automatic large-scale data pipeline that harvests high-qualityweakly-supervised answers from abundant Web data requiring only aquestion-reference pair as input. We study the efficacy and robustness of RWSin the setting of TANDA, a recent state-of-the-art fine-tuning approachspecialized for AS2. Our experiments indicate that the produced dataconsistently bolsters TANDA. We achieve the state of the art in terms of P@1,90.1%, and MAP, 92.9%, on WikiQA.

Reference-based Weak Supervision for Answer Sentence Selection using Web Data