Existing approaches to dialogue state tracking rely on pre-defined ontologies
consisting of a set of all possible slot types and values. Though such
approaches exhibit promising performance on single-domain benchmarks, they
suffer from computational complexity that increases proportionally to the
number of pre-defined slots that need tracking. This issue becomes more severe
when it comes to multi-domain dialogues which include larger numbers of slots.
In this paper, we investigate how to approach DST using a generation framework
without the pre-defined ontology list. Given each turn of user utterance and
system response, we directly generate a sequence of belief states by applying a
hierarchical encoder-decoder structure. In this way, the computational
complexity of our model will be a constant regardless of the number of
pre-defined slots. Experiments on both the multi-domain and the single domain
dialogue state tracking dataset show that our model not only scales easily with
the increasing number of pre-defined domains and slots but also reaches the
state-of-the-art performance.

通过使用分层编解码结构直接生成信念状态序列的方法，我们提出了一种不需要预定义本体列表的对话状态跟踪方法，并在多域和单域对话状态跟踪数据集上进行了实验，取得了最先进的性能。

通过分层序列生成实现可扩展和准确的对话状态跟踪

Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence  Generation

Robust dialogue belief tracking is a key component in maintaining good
quality dialogue systems. The tasks that dialogue systems are trying to solve
are becoming increasingly complex, requiring scalability to multi domain,
semantically rich dialogues. However, most current approaches have difficulty
scaling up with domains because of the dependency of the model parameters on
the dialogue ontology. In this paper, a novel approach is introduced that fully
utilizes semantic similarity between dialogue utterances and the ontology
terms, allowing the information to be shared across domains. The evaluation is
performed on a recently collected multi-domain dialogues dataset, one order of
magnitude larger than currently available corpora. Our model demonstrates great
capability in handling multi-domain dialogues, simultaneously outperforming
existing state-of-the-art models in single-domain dialogue tracking tasks.

本研究介绍了一种新颖的方法，该方法利用对话中的语义相似性和本体术语来跨领域地进行语音信仰跟踪，从而实现对多领域语音的处理，并在性能上优于现有的单领域跟踪任务的最先进模型。