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.

翻译：对话式问答（ConvQA）模型通常基于知识图谱（KG）上的标准问答对基准进行训练与评估。这意味着训练仅限于各自数据集中出现的表面形式，而评估仅针对少量保留的测试问题。通过我们提出的REIGN框架，我们采取若干步骤来改进这种受限的学习设置。首先，我们系统性地生成训练问题的改写版本，以增强模型对表面形式变化的鲁棒性。鉴于此类问题的碎片化特性，这是一个极具挑战性的任务。其次，我们利用深度强化学习，仅向对话式问答模型提供那些有助于提升回答质量的改写版本，从而引导其实现更高性能。第三，我们证明了在一个基准上训练主要模型组件并将其零样本迁移至另一基准的可行性。最后，为严格评估训练模型的鲁棒性，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版本（规模扩大20倍）。研究结果表明，通过改写进行鲁棒训练的对话式问答模型显著优于仅使用标准问答对进行训练的模型。