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首页> 外文会议>Pacific-Asia Conference on Knowledge Discovery and Data Mining >Fusing Essential Knowledge for Text-Based Open-Domain Question Answering
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Fusing Essential Knowledge for Text-Based Open-Domain Question Answering

机译：融合基于文本的开放式域问题的基本知识

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摘要

Question answering (QA) systems can be classified as either text-based QA systems or knowledge base QA (KBQA) systems, depending on the used knowledge source. KBQA systems are generally domain-specific and can't deal with a variety of questions in the open-domain QA setting, while text-based systems can. However, text-based systems' performance is far from satisfactory. This paper focuses on the text-based open-domain QA setting. We argue that text-based approaches' poor performance is largely caused by the lack of knowledge, which is often essential for answering the question and can be easily found in knowledge base (KB), in plain text. So in this paper, we propose a new text-based open-domain QA system called KF (Knowledge Fusion)-QA, which uses KB as a second knowledge source to incorporate essential knowledge into text to help answer the question. Our system has a Knowledge-Aware Encoder which extracts essential knowledge from KB and performs knowledge fusion to output knowledge-aware (KA) text representations. With this KA representations, the system first re-rank the retrieved documents, then read the re-ranked top-N documents to give the answer. Our system significantly outperforms existing text-based QA systems on multiple open-domain QA datasets, demonstrating the effectiveness of fusing essential knowledge.
机译：问题应答（QA）系统可以归类为基于文本的QA系统或知识库QA（KBQA）系统，具体取决于所使用的知识源。 KBQA系统通常是特定于域的，无法在开放式域QA设置中处理各种问题，而基于文本的系统可以。但是，基于文本的系统的性能远非令人满意。本文重点介绍基于文本的开放式QA设置。我们认为基于文本的方法的表现不佳主要是由于缺乏知识造成的，这对于回答问题通常是必不可少的，并且在纯文本中可以很容易地在知识库（KB）中找到。因此，在本文中，我们提出了一种名为KF（知识融合）-QA的新文本的开放式QA系统，该系统使用KB作为第二个知识源，将基本知识纳入文本以帮助回答问题。我们的系统具有知识感知的编码器，其从KB提取基本知识，并执行知识融合以输出知识感知（KA）文本表示。使用此KA表示，系统首先重新排名检索到的文档，然后读取重新排名的TOP-N文档以提供答案。我们的系统在多个开放式QA数据集上显着优于现有的基于文本的QA系统，展示了融合基本知识的有效性。

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