论文标题
Tiara：在大型知识基础上回答有力的问题的多元元素检索
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
论文作者
论文摘要
预训练的语言模型（PLM）在多种情况下显示出其有效性。但是，KBQA仍然具有挑战性，尤其是在覆盖范围和概括环境方面。这是由于两个主要因素造成的：i）了解KB的问题和相关知识的语义； ii）具有语义和句法正确性的可执行逻辑形式。在本文中，我们提出了一种新的KBQA模型Tiara，该模型通过应用多层次的检索来帮助PLM专注于最相关的KB上下文，即实体，实体，示例逻辑形式和模式项目来解决这些问题。此外，使用受限的解码用于控制输出空间并减少发电错误。对重要基准的实验证明了我们方法的有效性。 Tiara的表现分别优于先前的SOTA，包括使用PLMS或Oracle Entity注释的SOTA，分别在GrailQA和WebQuestionsSp上至少高4.1和1.1 F1点。
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios. However, KBQA remains challenging, especially regarding coverage and generalization settings. This is due to two main factors: i) understanding the semantics of both questions and relevant knowledge from the KB; ii) generating executable logical forms with both semantic and syntactic correctness. In this paper, we present a new KBQA model, TIARA, which addresses those issues by applying multi-grained retrieval to help the PLM focus on the most relevant KB contexts, viz., entities, exemplary logical forms, and schema items. Moreover, constrained decoding is used to control the output space and reduce generation errors. Experiments over important benchmarks demonstrate the effectiveness of our approach. TIARA outperforms previous SOTA, including those using PLMs or oracle entity annotations, by at least 4.1 and 1.1 F1 points on GrailQA and WebQuestionsSP, respectively.
