多跳问题生成任务旨在聚合多段离散信息进行复杂推理并生成自然语言的问句。对于给定的问答对,文本中多数句子都是冗余或含有不相关信息的句子,而之前大多数方法在模型的训练和应用推断中都需要提前标注好的句级标签。然而,大规模的句子标注数据在现实场景中是难以获取的。为了解决这一问题,该文提出一种基于佐证句选择的图神经网络(Graph-based Evidence Selection network,GES)。该模型通过图神经网络从离散文档中提取出若干个关键句,然后根据对应结果引入归纳偏置来辅助问题生成。同时采用直通估计量(straight-through estimator)来端到端地训练模型。在公开数据集HotpotQA的对比实验中,该方法在问题生成的多个指标上均取得了显著的性能提升。
Abstract
Multi-hop Question Generation is the task of reasoning over disjoint pieces of information and then generating complex questions. For the given Q&A pair, the context contains a large number of redundant and irrelevant sentences, and most previous methods require annotated corpus to select supporting facts as input to generate corresponding questions. To address this problem, this paper proposes a Graph-based Evidence Selection network (GES) for deep question generation over documents. The proposed model selects informative sentences from disjointed paragraphs, which serves as an inductive bias to refine question generation. We also employ a straight-through estimator to train the model in an end-to-end manner. Experimental results on the HotpotQA dataset demonstrate that our proposed solution outperforms state-of-the-art methods by a significant margin.
关键词
问题生成 /
图神经网络 /
直通估计量
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Key words
question generation /
graph neural network /
straight-through estimator
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