论文标题
了解您所知道的：通过合奏校准对话信念状态分布
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles
论文作者
论文摘要
准确跟踪对话中发生的事情的能力对于对话系统的性能至关重要。当前最新的多域对话状态跟踪器在当前的基准标准上实现了超过55％的准确性，这意味着，在几乎每一秒的对话中，他们都对不正确的对话状态充满信心。另一方面，信仰跟踪器在可能的对话中保持分布。但是，与对话状态跟踪器相比，它们缺乏性能，并且不会产生良好的校准分布。在这项工作中，我们介绍了使用校准模型集合的多域对话信念跟踪器进行校准的最新性能。我们由此产生的对话信念跟踪器还胜过以前的对话信念跟踪模型，以准确性。
The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system. Current state-of-the-art multi-domain dialogue state trackers achieve just over 55% accuracy on the current go-to benchmark, which means that in almost every second dialogue turn they place full confidence in an incorrect dialogue state. Belief trackers, on the other hand, maintain a distribution over possible dialogue states. However, they lack in performance compared to dialogue state trackers, and do not produce well calibrated distributions. In this work we present state-of-the-art performance in calibration for multi-domain dialogue belief trackers using a calibrated ensemble of models. Our resulting dialogue belief tracker also outperforms previous dialogue belief tracking models in terms of accuracy.
