论文标题
会话问题回答的流利响应生成
Fluent Response Generation for Conversational Question Answering
论文作者
论文摘要
问答（QA）是开放域对话剂的重要方面，在对话质量质量检查（CONSQA）子任务中获得了特定的研究重点。最近的ConvQA工作的一个值得注意的局限性是响应是从目标语料库中提取回答，因此忽略了高质量对话剂的自然语言产生（NLG）方面。在这项工作中，我们提出了一种在SEQ2SEQ NLG方法中列出质量检查响应的方法，以在保持正确性的同时产生流利的语法答案。从技术角度来看，我们使用数据扩展为端到端系统生成培训数据。具体而言，我们开发句法变换（STS）来产生特定问题的候选答案，并使用基于BERT的分类器对它们进行排名（Devlin等，2019）。人类对小队2.0数据的评估（Rajpurkar等，2018）表明，所提出的模型在产生对话响应时优于基线COQA和QUAC模型。我们通过在COQA数据集上进行测试进一步显示了模型的可伸缩性。代码和数据可在https://github.com/abaheti95/qadialogsystem上获得。
Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA (ConvQA) subtask. One notable limitation of recent ConvQA efforts is the response being answer span extraction from the target corpus, thus ignoring the natural language generation (NLG) aspect of high-quality conversational agents. In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. From a technical perspective, we use data augmentation to generate training data for an end-to-end system. Specifically, we develop Syntactic Transformations (STs) to produce question-specific candidate answer responses and rank them using a BERT-based classifier (Devlin et al., 2019). Human evaluation on SQuAD 2.0 data (Rajpurkar et al., 2018) demonstrate that the proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. We further show our model's scalability by conducting tests on the CoQA dataset. The code and data are available at https://github.com/abaheti95/QADialogSystem.
