Abstract
Taking advantage of the rapid growth of community platforms, such as Yahoo Answers, Quora, etc., Community Question Answering (CQA) systems are developed to retrieve semantically equivalent questions when users raise a new query. A typical CQA system mainly consists of two key components, a retrieval model and a ranking model, to search for similar questions and select the most related, respectively. In this paper, we propose LARQ, Learning to Ask and Rewrite Questions, which is a novel sentence-level data augmentation method. Different from common lexical-level data augmentation progresses, we take advantage of the Question Generation (QG) model to obtain more accurate, diverse, and semantically-rich query examples. Since the queries differ greatly in a low-resource code-start scenario, incorporating the QG model as an augmentation to the indexed collection significantly improves the response rate of CQA systems. We incorporate LARQ in an online CQA system and the Bank Question (BQ) Corpus to evaluate the enhancements for both the retrieval process and the ranking model. Extensive experimental results show that the LARQ enhanced model significantly outperforms single BERT and XGBoost models, as well as a widely-used QG model (NQG).
H. Zhou and H. Liu—Equal Contributions.
H. Liu—Work done during an internship at Tencent.
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Zhou, H., Liu, H., Yan, Z., Cao, Y., Li, Z. (2020). LARQ: Learning to Ask and Rewrite Questions for Community Question Answering. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_26
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