论文标题
Arcaneqa：动态程序归纳和情境化编码知识基础问题回答
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering
论文作者
论文摘要
在知识库（KBQA）上回答的问题对语义解析研究构成了一个独特的挑战，这是由于两个相互交织的挑战：大型搜索空间和模式链接中的模棱两可。基于常规排名的KBQA模型依靠候选人枚举步骤来减少搜索空间，在预测复杂的查询方面具有灵活性并具有不切实际的运行时间。在本文中，我们提出了Arcaneqa，这是一个基于一代新颖的模型，该模型既解决统一框架中的较大搜索空间和架构，并将挑战与两种相互促进成分联系起来：动态程序诱导，以解决大型搜索空间和动态上下文化的编码，以链接模式。多个流行KBQA数据集的实验结果证明了Arcaneqa在有效性和效率方面的竞争性表现高。
Question answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking. Conventional ranking-based KBQA models, which rely on a candidate enumeration step to reduce the search space, struggle with flexibility in predicting complicated queries and have impractical running time. In this paper, we present ArcaneQA, a novel generation-based model that addresses both the large search space and the schema linking challenges in a unified framework with two mutually boosting ingredients: dynamic program induction for tackling the large search space and dynamic contextualized encoding for schema linking. Experimental results on multiple popular KBQA datasets demonstrate the highly competitive performance of ArcaneQA in both effectiveness and efficiency.
