KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
复制标题
KG-FiD：将知识图注入 Fusion-in-Decoder 中以实现开放域问答
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Michael Zeng
中科院分区：
文献类型：
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作者：
Donghan Yu;Chenguang Zhu;Yuwei Fang;W. Yu;Shuohang Wang;Yichong Xu;Xiang Ren;Yiming Yang;Michael Zeng
当前的开放领域问题检索（ODQA）模型通常包括检索模块和阅读模块，其中检索者从开放源文档中为给定的问题选择潜在相关的段落，并且读者基于检索到的段落产生答案。最近提出的融合解码器（FiD）框架是一个代表性的例子，它是建立在一个密集的通道检索器和生成的读者，实现了最先进的性能。在本文中，我们进一步提高了FiD的方法，通过引入知识增强的版本，即KG FiD。我们的新模型使用知识图来建立检索到的段落之间的结构关系，并使用图神经网络（GNN）对段落进行重新排序，并只选择前几个进行进一步处理。我们在常见的ODQA基准数据集（Natural Questions和TriviaQA）上的实验表明，KG-FiD在答案预测方面可以达到与FiD相当或更好的性能，而计算成本不到40%。
Current Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module, where the retriever selects potentially relevant passages from open-source documents for a given question, and the reader produces an answer based on the retrieved passages. The recently proposed Fusion-in-Decoder (FiD) framework is a representative example, which is built on top of a dense passage retriever and a generative reader, achieving the state-of-the-art performance. In this paper we further improve the FiD approach by introducing a knowledge-enhanced version, namely KG-FiD. Our new model uses a knowledge graph to establish the structural relationship among the retrieved passages, and a graph neural network (GNN) to re-rank the passages and select only a top few for further processing. Our experiments on common ODQA benchmark datasets (Natural Questions and TriviaQA) demonstrate that KG-FiD can achieve comparable or better performance in answer prediction than FiD, with less than 40% of the computation cost.
影响因子：
22.7
作者：
Vrandecic, Denny;Kroetzsch, Markus
通讯作者：
Kroetzsch, Markus