Sequence-to-sequence state-of-the-art systems for dialogue state tracking
(DST) use the full dialogue history as input, represent the current state as a
list with all the slots, and generate the entire state from scratch at each
dialogue turn. This approach is inefficient, especially when the number of
slots is large and the conversation is long. In this paper, we propose Diable,
a new task formalisation that simplifies the design and implementation of
efficient DST systems and allows one to easily plug and play large language
models. We represent the dialogue state as a table and formalise DST as a table
manipulation task. At each turn, the system updates the previous state by
generating table operations based on the dialogue context. Extensive
experimentation on the MultiWoz datasets demonstrates that Diable (i)
outperforms strong efficient DST baselines, (ii) is 2.4x more time efficient
than current state-of-the-art methods while retaining competitive Joint Goal
Accuracy, and (iii) is robust to noisy data annotations due to the table
operations approach.

本文提出了 Diable 系统，通过将对话状态表示为数据表，并将 DST 形式化为表操作任务，简化了设计和实施高效的 DST 系统，通过在 MultiWoz 数据集上的实验证明，Diable 系统优于强大的高效 DST 基线，比当前最先进的方法更节省时间，而具有良好的鲁棒性，可以处理由于数据注释噪声导致表操作问题。

Diable: 基于表格操作的高效对话状态追踪

Diable: Efficient Dialogue State Tracking as Operations on Tables

This paper presents our task-oriented dialog system UBAR which models
task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired
by fine-tuning the large pre-trained unidirectional language model GPT-2 on the
sequence of the entire dialog session which is composed of user utterance,
belief state, database result, system act, and system response of every dialog
turn. Additionally, UBAR is evaluated in a more realistic setting, where its
dialog context has access to user utterances and all content it generated such
as belief states, system acts, and system responses. Experimental results on
the MultiWOZ datasets show that UBAR achieves state-of-the-art performances in
multiple settings, improving the combined score of response generation, policy
optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively.
Thorough analyses demonstrate that the session-level training sequence
formulation and the generated dialog context are essential for UBAR to operate
as a fully end-to-end task-oriented dialog system in real life. We also examine
the transfer ability of UBAR to new domains with limited data and provide
visualization and a case study to illustrate the advantages of UBAR in modeling
on a dialog session level.

该研究介绍了一种基于任务的对话系统 UBAR，使用 GPT-2 对话会话级别的序列进行微调，实现了任务导向对话的完全端对端建模，能在多种设置下取得领先的性能。

UBAR：基于 GPT-2 的全端到端任务导向对话系统

UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2

Recent studies in dialogue state tracking (DST) leverage historical
information to determine states which are generally represented as slot-value
pairs. However, most of them have limitations to efficiently exploit relevant
context due to the lack of a powerful mechanism for modeling interactions
between the slot and the dialogue history. Besides, existing methods usually
ignore the slot imbalance problem and treat all slots indiscriminately, which
limits the learning of hard slots and eventually hurts overall performance. In
this paper, we propose to enhance the DST through employing a contextual
hierarchical attention network to not only discern relevant information at both
word level and turn level but also learn contextual representations. We further
propose an adaptive objective to alleviate the slot imbalance problem by
dynamically adjust weights of different slots during training. Experimental
results show that our approach reaches 52.68% and 58.55% joint accuracy on
MultiWOZ 2.0 and MultiWOZ 2.1 datasets respectively and achieves new
state-of-the-art performance with considerable improvements (+1.24% and
+5.98%).

该研究提出了使用上下文分层关注网络（CHAN）以及自适应目标策略来加强对话状态追踪（DST）模型，解决了文本中存在的关于历史对话、不平衡的 slot 分配、模型上下文表示等问题，实现了具有很大提升的端到端追踪的性能。