Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian Vlad Serban∘∗{{}^{*}}{{}^{\circ}}、Alberto García-Durán⋄∗{{}^{*}}{{}^{\diamond}}、Caglar Gulcehre∘、Sungjin Ahn∘ \ANDSarath Chandar∘、Aaron Courville∘、Yoshua Bengio∘†{{}^{\dagger}}{{}^{\circ}}
Abstract
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M Factoid Question-Answer Corpus, an enormous question answer pair corpus produced by applying a novel neural network architecture on the knowledge base Freebase to transduce facts into natural language questions. The produced question answer pairs are evaluated both by human evaluators and using automatic evaluation metrics, including well-established machine translation and sentence similarity metrics. Across all evaluation criteria the question-generation model outperforms the competing template-based baseline. Furthermore, when presented to human evaluators, the generated questions appear comparable in quality to real human-generated questions.
中文速览
问答系统长期面临一个卡脖子难题：缺乏大规模标注的问答对数据来训练模型。研究者们借鉴神经机器翻译的思路，把知识库Freebase中的三元组事实（主体、关系、客体）当作"源语言"，用带注意力机制的编码器-解码器网络将其"翻译"成自然语言问题，客体即为对应答案。为了解决实体嵌入稀疏的问题，他们预先用TransE模型在整个Freebase上学习实体和关系的向量表示，再喂给编码器，使模型能泛化到训练集中未见过的实体。最终，该方法在BLEU、METEOR等自动评测指标上显著优于基于模板的基线方法，人工评测也表明生成的问题质量与真人提问难以区分，研究者据此构建了包含3000万问答对的"30M Factoid问答语料库"并公开发布，为训练大规模问答系统提供了此前从未有过的数据基础。
原文 arXiv:1603.06807;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1603.06807v2