论文标题
Luna：学习对话状态跟踪的学习插槽转动对齐
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking
论文作者
论文摘要
对话状态跟踪（DST）旨在预测对话历史的当前对话状态。现有方法通常会利用所有对话的说法，以分配每个插槽的值。由于对话历史中无关的话语所带来的信息，这可能会导致次优的结果，这可能是无用的，甚至可能引起混乱。为了解决这个问题，我们提出了Luna，这是一种插槽变动对齐的增强方法。首先，它明确地将每个插槽与最相关的话语保持一致，然后进一步预测基于这种统一的话语而不是所有对话话语的相应价值。此外，我们设计了一个插槽排名辅助任务，以了解插槽之间的时间相关性，这可能有助于对齐。全面实验是在多域任务对话数据集上进行的，即Multiwoz 2.0，Multiwoz 2.1和Multiwoz 2.2。结果表明，Luna在这些数据集上实现了新的最新结果。
Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history. Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot. This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion. To address this problem, we propose LUNA, a sLot-tUrN Alignment enhanced approach. It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances. Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment. Comprehensive experiments are conducted on multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art results on these datasets.
