论文标题
旨在利用潜在的知识和对话环境来实现现实世界的对话问题回答
Towards leveraging latent knowledge and Dialogue context for real-world conversational question answering
论文作者
论文摘要
在许多实际情况下，缺乏像Wikipedia这样的外部知识来源限制了问题答案系统在有限的对话数据中依靠潜在的内部知识。此外，人类经常通过提出几个问题以获取更多全面信息来寻求答案。随着对话框变得更加广泛，挑战机器要参考以前的对话回答以回答问题。在这项工作中，我们建议通过神经检索阅读系统利用现有对话日志中的潜在知识，并通过基于TFIDF的文本摘要来增强，以完善冗长的对话历史，以减轻长篇小说问题。我们的实验表明，我们的检索阅读系统可以利用检索的背景知识来产生更好的答案。结果还表明，我们的上下文摘要器可以通过引入更多简洁且较少的嘈杂上下文信息来大大帮助猎犬和读者。
In many real-world scenarios, the absence of external knowledge source like Wikipedia restricts question answering systems to rely on latent internal knowledge in limited dialogue data. In addition, humans often seek answers by asking several questions for more comprehensive information. As the dialog becomes more extensive, machines are challenged to refer to previous conversation rounds to answer questions. In this work, we propose to leverage latent knowledge in existing conversation logs via a neural Retrieval-Reading system, enhanced with a TFIDF-based text summarizer refining lengthy conversational history to alleviate the long context issue. Our experiments show that our Retrieval-Reading system can exploit retrieved background knowledge to generate significantly better answers. The results also indicate that our context summarizer significantly helps both the retriever and the reader by introducing more concise and less noisy contextual information.
