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

One of the core components of modern spoken dialogue systems is the belief
tracker, which estimates the user's goal at every step of the dialogue.
However, most current approaches have difficulty scaling to larger, more
complex dialogue domains. This is due to their dependency on either: a) Spoken
Language Understanding models that require large amounts of annotated training
data; or b) hand-crafted lexicons for capturing some of the linguistic
variation in users' language. We propose a novel Neural Belief Tracking (NBT)
framework which overcomes these problems by building on recent advances in
representation learning. NBT models reason over pre-trained word vectors,
learning to compose them into distributed representations of user utterances
and dialogue context. Our evaluation on two datasets shows that this approach
surpasses past limitations, matching the performance of state-of-the-art models
which rely on hand-crafted semantic lexicons and outperforming them when such
lexicons are not provided.

提出了一种利用预训练词向量进行表示学习的神经信念追踪（Neural Belief Tracking，NBT）框架，用于估计对话中用户的目标，并可以在不使用手工制作语义词典的情况下达到与最先进的模型相同或更好的效果。