A common thread of open-domain question answering (QA) models employs a retriever-reader pipeline that first retrieves a handful of relevant passages from Wikipedia and then peruses the passages to produce an answer. However, even state-of-the-art readers fail to capture the complex relationships between entities appearing in questions and retrieved passages, leading to answers that contradict the facts. In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA. Specifically, for each pair of question and retrieved passage, we first construct a localized bipartite graph, attributed to entity embeddings extracted from the intermediate layer of the reader model. Then, a graph neural network learns relational knowledge while fusing graph and contextual representations into the hidden states of the reader model. Experiments on three open-domain QA benchmarks show Grape can improve the state-of-the-art performance by up to 2.2 exact match score with a negligible overhead increase, with the same retriever and retrieved passages. Our code is publicly available at https://github.com/jumxglhf/GRAPE.
翻译:开放式问题解答(QA)模式的一条共同的开放域解答( QA) 模式使用一个检索器读取器管道, 首先从维基百科取取取一小撮相关段落, 然后对段落进行仔细检查以得出答案。 然而, 即使是最先进的阅读器也未能捕捉在问题和检索段落中出现的实体之间的复杂关系, 从而得出与事实相矛盾的答案。 有鉴于此, 我们建议使用一个新的知识图强化读取器, 即 Grape, 来改善开放式域解答( QA) 的读者性能。 具体地说, 每对问题和检索通道, 我们首先建立一个本地化的双部分图, 归结于从读者模式中间层提取的实体。 然后, 一个图形神经网络在使用图形和背景表达方式进入读者模型的隐藏状态的同时学习关系性知识。 在三个开放域 QA 基准上进行的实验显示 Grape 能够改进状态- 艺术性能达到2.2 精确匹配分, 与微不足道的间接增加值, 并检索器和检索通道。 我们的代码在 http:// RAPG/ comxghl可以公开查阅 。