论文标题
对话状态跟踪的顺序到序列方法
A Sequence-to-Sequence Approach to Dialogue State Tracking
论文作者
论文摘要
本文与以任务为导向的对话系统中的对话状态跟踪（DST）有关。尽管最近取得了重大进展，但建立一个非常有效的DST模块仍然是一个具有挑战性的问题。本文提出了一种新的对话状态跟踪方法，称为seq2seq-du，将DST形式化为序列到序列问题。 SEQ2SEQ-DU采用两个基于BERT的编码器来分别编码对话中的话语和架构的描述，这是一个计算说话嵌入和模式嵌入之间注意的观察者，以及一个解码器，以生成指针来代表对话的情况。 SEQ2SEQ-DU具有以下优势。它可以共同建模意图，插槽和插槽值。它可以利用基于伯特的话语和模式的丰富表示。它可以有效地处理分类和非类别插槽以及看不见的模式。此外，SEQ2SEQ-DU也可以用于对话系统的NLU（自然语言理解）模块中。在不同设置（SGD，MultiWoz2.2，MultiWoz2.1，Woz2.0，DSTC2，M2M，SNIPS和ATIS）中基准数据集的实验结果表明SEQ2SEQ-DU优于现有方法。
This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made recently. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model intents, slots, and slot values; it can leverage the rich representations of utterances and schemas based on BERT; it can effectively deal with categorical and non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, MultiWOZ2.1, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.
