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.

本文提出了一种基于模型集成的多领域对话信念跟踪器，其在校准方面表现优异，且相对于先前的对话信念跟踪模型在准确性方面表现更佳。

通过集成方法校准对话信念状态分布

Knowing What You Know: Calibrating Dialogue Belief State Distributions  via Ensembles

Neural dialog state trackers are generally limited due to the lack of
quantity and diversity of annotated training data. In this paper, we address
this difficulty by proposing a reinforcement learning (RL) based framework for
data augmentation that can generate high-quality data to improve the neural
state tracker. Specifically, we introduce a novel contextual bandit generator
to learn fine-grained augmentation policies that can generate new effective
instances by choosing suitable replacements for the specific context. Moreover,
by alternately learning between the generator and the state tracker, we can
keep refining the generative policies to generate more high-quality training
data for neural state tracker. Experimental results on the WoZ and MultiWoZ
(restaurant) datasets demonstrate that the proposed framework significantly
improves the performance over the state-of-the-art models, especially with
limited training data.

通过引入一种基于强化学习的框架和上下文新手发电机来解决神经对话状态追踪器的训练数据缺乏数量和多样性的问题，通过训练产生更多的高质量数据，极大地提高了神经状态追踪器的效果。