论文标题
Crake：通过大规模知识库回答问题的因果增强的桌上填充者
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base
论文作者
论文摘要
语义解析通过组成KB查询来求解知识库（KB）问题回答（KBQA），该查询通常涉及节点提取（NE）和图形组成（GC）以检测和连接查询中的相关节点。尽管NE和GC之间具有强烈的因果作用，但先前的作品未能直接建模其管道中的这种因果关系，从而阻碍了学习子任务相关性的学习。同样，以前作品中GC的序列产生过程会引起歧义和暴露偏见，从而进一步损害准确性。在这项工作中，我们将语义解析正式分为两个阶段。在第一阶段（图结构生成）中，我们提出了一个因果增强的桌面填充者，以克服序列模型的问题并学习内部因果关系。在第二阶段（关系提取）中，提出了一种有效的梁搜索算法以在大规模KBS上扩展复杂查询。 LC-Quad 1.0的实验表明，我们的方法超过了先前的最新边距（17％），同时剩余的时间和空间效率。代码和型号可在https://github.com/aozmh/crake上找到。
Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect and connect related nodes in a query. Despite the strong causal effects between NE and GC, previous works fail to directly model such causalities in their pipeline, hindering the learning of subtask correlations. Also, the sequence-generation process for GC in previous works induces ambiguity and exposure bias, which further harms accuracy. In this work, we formalize semantic parsing into two stages. In the first stage (graph structure generation), we propose a causal-enhanced table-filler to overcome the issues in sequence-modelling and to learn the internal causalities. In the second stage (relation extraction), an efficient beam-search algorithm is presented to scale complex queries on large-scale KBs. Experiments on LC-QuAD 1.0 indicate that our method surpasses previous state-of-the-arts by a large margin (17%) while remaining time and space efficiency. The code and models are available at https://github.com/AOZMH/Crake.
