论文标题
多跳问题的加强多任务方法
Reinforced Multi-task Approach for Multi-hop Question Generation
论文作者
论文摘要
问题生成（QG）试图通过产生文档和答案来解决自然语言问题来解决问题回答（QA）问题的倒数。序列神经模型的序列超过了基于QG的规则的系统，但它们的能力限制了专注于多个支持事实的能力。对于QG，我们通常需要多个支持事实来产生高质量的问题。受QA中多跳推理的最新作品的启发，我们进行了多跳问题的生成，该问题旨在基于背景下支持事实的相关问题。我们采用多任务学习，辅助任务是回答意识支持事实预测以指导问题生成器。此外，我们还提出了在增强学习（RL）框架中提出的问题感知奖励功能，以最大程度地利用支持事实。我们通过对回答数据集HotPotQA的多跳问题进行实验来证明我们的方法的有效性。经验评估表明，我们的模型在自动评估指标（例如BLEU，流星和胭脂）上都超过单跳神经问题的产生模型，以及人类评估指标，以质量和覆盖所产生的问题的质量和覆盖率。
Question generation (QG) attempts to solve the inverse of question answering (QA) problem by generating a natural language question given a document and an answer. While sequence to sequence neural models surpass rule-based systems for QG, they are limited in their capacity to focus on more than one supporting fact. For QG, we often require multiple supporting facts to generate high-quality questions. Inspired by recent works on multi-hop reasoning in QA, we take up Multi-hop question generation, which aims at generating relevant questions based on supporting facts in the context. We employ multitask learning with the auxiliary task of answer-aware supporting fact prediction to guide the question generator. In addition, we also proposed a question-aware reward function in a Reinforcement Learning (RL) framework to maximize the utilization of the supporting facts. We demonstrate the effectiveness of our approach through experiments on the multi-hop question answering dataset, HotPotQA. Empirical evaluation shows our model to outperform the single-hop neural question generation models on both automatic evaluation metrics such as BLEU, METEOR, and ROUGE, and human evaluation metrics for quality and coverage of the generated questions.
