论文标题
图形卷积网络的多跳问题生成
Multi-hop Question Generation with Graph Convolutional Network
论文作者
论文摘要
多跳问题生成（QG）旨在通过在不同段落中汇总和推理来产生与答案相关的问题。与传统的单跳QG相比，这是一个更具挑战性但爆炸式爆炸性的任务，其中这些问题是从包含答案或附近句子附近的句子中生成的，而没有复杂的推理。为了解决多跳QG中的其他挑战，我们提出了编码问题生成的融合网络（MULQG）的多跳型融合网络，该网络确实通过图形卷积网络在多个啤酒花中编码上下文，并通过编码器推理门编码融合。据我们所知，我们是第一个解决有关段落的多跳推理挑战的人，而无需任何句子级别的信息。与基准相比，HOTPOTQA数据集的经验结果证明了我们方法的有效性。此外，从人类评估中，我们提出的模型能够以高度完整性产生流利的问题，并在多跳评估中优于最强的基线20.8％。该代码可在https://github.com/hltchkust/mulqg} {https://github.com/hltchkust/mulqg上公开获得。
Multi-hop Question Generation (QG) aims to generate answer-related questions by aggregating and reasoning over multiple scattered evidence from different paragraphs. It is a more challenging yet under-explored task compared to conventional single-hop QG, where the questions are generated from the sentence containing the answer or nearby sentences in the same paragraph without complex reasoning. To address the additional challenges in multi-hop QG, we propose Multi-Hop Encoding Fusion Network for Question Generation (MulQG), which does context encoding in multiple hops with Graph Convolutional Network and encoding fusion via an Encoder Reasoning Gate. To the best of our knowledge, we are the first to tackle the challenge of multi-hop reasoning over paragraphs without any sentence-level information. Empirical results on HotpotQA dataset demonstrate the effectiveness of our method, in comparison with baselines on automatic evaluation metrics. Moreover, from the human evaluation, our proposed model is able to generate fluent questions with high completeness and outperforms the strongest baseline by 20.8% in the multi-hop evaluation. The code is publicly available at https://github.com/HLTCHKUST/MulQG}{https://github.com/HLTCHKUST/MulQG .
