基于强化改写生成的对话式问答模型鲁棒训练
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation
AI总结：
提出框架REIGN，通过系统生成问题改写、利用深度强化学习筛选有益改写并支持零样本迁移，显著提升了基于知识图谱的对话式问答模型对表层形式变化的鲁棒性。
摘要：
基于知识图谱（KGs）的对话式问答（ConvQA）模型通常在黄金QA对的基准上进行训练和测试。这意味着训练仅限于各自数据集中出现的表层形式，而评估则在一小部分留出问题上进行。通过我们提出的框架REIGN，我们采取了几个步骤来纠正这种受限的学习设置。首先，我们系统地生成训练问题的改写，以提高模型对表层形式变化的鲁棒性。鉴于此类问题具有不完整的性质，这是一个特别具有挑战性的问题。其次，我们利用深度强化学习，仅向ConvQA模型输入有助于提高其回答质量的改写，从而引导其获得更高的性能。第三，我们展示了在一个基准上训练主要模型组件并将其零样本应用于另一个基准的可行性。最后，为了对训练模型的鲁棒性进行严格评估，我们使用并发布了通过提示GPT为基准测试集生成的大量多样化改写（导致规模增加20倍）。我们的研究结果表明，通过改写进行鲁棒训练的ConvQA模型，显著优于仅使用黄金QA对进行标准训练的模型。
英文摘要：
Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions. Through our proposed framework REIGN, we take several steps to remedy this restricted learning setup. First, we systematically generate reformulations of training questions to increase robustness of models to surface form variations. This is a particularly challenging problem, given the incomplete nature of such questions. Second, we guide ConvQA models towards higher performance by feeding it only those reformulations that help improve their answering quality, using deep reinforcement learning. Third, we demonstrate the viability of training major model components on one benchmark and applying them zero-shot to another. Finally, for a rigorous evaluation of robustness for trained models, we use and release large numbers of diverse reformulations generated by prompting GPT for benchmark test sets (resulting in 20x increase in sizes). Our findings show that ConvQA models with robust training via reformulations, significantly outperform those with standard training from gold QA pairs only.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
