Existing knowledge-based question answering systems often rely on small
annotated training data. While shallow methods like relation extraction are
robust to data scarcity, they are less expressive than the deep meaning
representation methods like semantic parsing, thereby failing at answering
questions involving multiple constraints. Here we alleviate this problem by
empowering a relation extraction method with additional evidence from
Wikipedia. We first present a neural network based relation extractor to
retrieve the candidate answers from Freebase, and then infer over Wikipedia to
validate these answers. Experiments on the WebQuestions question answering
dataset show that our method achieves an F_1 of 53.3%, a substantial
improvement over the state-of-the-art.

本文提出一种从 Wikipedia 中提取证据丰富的关系抽取器，通过使用神经网络构建的关系提取器并结合 Wikipedia 进行验证，本方法成功地解决了现有知识型问答系统数据不足、缺乏表达力和无法回答多重约束问题等问题，并在 WebQuestions 问答数据集上取得了 53.3% 的 F1 值，比现有最先进的方法有了显著提升。