论文标题
答案是通过知识图的对话问题答案的语言数据集
An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs
论文作者
论文摘要
我们介绍了一个新数据集，以通过口头答案对知识图（KGS）回答对话问题。目前，对KGS的问题回答是针对单转问题的答案（KGQA）或多型对话对话问题答案（Convqa）。但是，在现实情况下（例如，Siri，Alexa和Google Assistant等语音助手），用户更喜欢口头上的答案。本文通过扩展了具有多种释义的言语答案的现有Convqa数据集，从而为最先进的方法做出了贡献。我们使用五个序列到序列模型进行实验，以生成答案响应，同时保持语法正确性。我们还执行错误分析，详细介绍了指定类别中模型错误预测的速率。我们提出的随着答案语言扩展的数据集可公开使用，其中包含有关其更广泛用途的详细文档。
We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g., voice assistants such as Siri, Alexa, and Google Assistant), users prefer verbalized answers. This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with multiple paraphrased verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness. We additionally perform an error analysis that details the rates of models' mispredictions in specified categories. Our proposed dataset extended with answer verbalization is publicly available with detailed documentation on its usage for wider utility.
