论文标题
XQA-DST：多域和多语言对话状态跟踪
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking
论文作者
论文摘要
对话状态跟踪（DST）是以任务为导向对话（TOD）系统的关键组成部分，它跟踪与对话历史有关的所有重要信息：在整个对话中填充插槽中具有最可能的值。现有方法通常依赖于预定义的一组值，并难以推广到新域中的以前看不见的插槽。为了克服这些挑战，我们提出了一种域 - 不可思议的提取问题答案（QA）方法，并具有跨域的共同权重。为了删除TOD中的复杂域信息，我们通过排除室外问题样本来通过新的域过滤策略来训练DST。通过一个独立的分类器，可以通过在有效域中提取跨度来预测多个域的存在，从而解决DST。经验结果表明，我们的模型可以通过两阶段的微调有效地利用域 - 不合命中的QA数据集，而在DST中既是域可估算和开放式摄影剂。它通过在Multiwoz 2.1上实现零射击域适应性结果，平均JGA为36.7％，显示出强大的可传递性。它进一步实现了跨语性转移，最先进的零球结果，66.2％的JGA从英语到德语，在WOZ 2.0上从英语到意大利的75.7％JGA。
Dialogue State Tracking (DST), a crucial component of task-oriented dialogue (ToD) systems, keeps track of all important information pertaining to dialogue history: filling slots with the most probable values throughout the conversation. Existing methods generally rely on a predefined set of values and struggle to generalise to previously unseen slots in new domains. To overcome these challenges, we propose a domain-agnostic extractive question answering (QA) approach with shared weights across domains. To disentangle the complex domain information in ToDs, we train our DST with a novel domain filtering strategy by excluding out-of-domain question samples. With an independent classifier that predicts the presence of multiple domains given the context, our model tackles DST by extracting spans in active domains. Empirical results demonstrate that our model can efficiently leverage domain-agnostic QA datasets by two-stage fine-tuning while being both domain-scalable and open-vocabulary in DST. It shows strong transferability by achieving zero-shot domain-adaptation results on MultiWOZ 2.1 with an average JGA of 36.7%. It further achieves cross-lingual transfer with state-of-the-art zero-shot results, 66.2% JGA from English to German and 75.7% JGA from English to Italian on WOZ 2.0.
