论文标题
一个概率的端到端，面向任务的对话模型，其潜在信念状态针对半监督学习
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning
论文作者
论文摘要
结构化信念状态对于以任务为导向的对话系统中的用户目标跟踪和数据库查询至关重要。但是，培训信念跟踪器通常需要每个用户话语的昂贵转向级注释。在本文中，我们旨在通过利用未标记的对话数据数据来减轻构建端到端对话系统中对信仰状态标签的依赖。我们提出了一个概率对话模型，称为潜在信念状态（LAB）模型，其中信念状态表示为离散的潜在变量，并以给定用户输入给定的系统响应共同建模。这种潜在的可变建模使我们能够在原则上的变分学习框架下开发半监督的学习。此外，我们介绍了Labes-S2S，这是LABES的副本SEQ2SEQ模型实例化。在监督实验中，Labes-S2S在不同尺度的三个基准数据集上获得了强劲的结果。在利用未标记的对话数据时，半监督的Labes-S2S显着优于仅监督和半监督的基线。值得注意的是，我们可以将注释需求减少到50％，而不会对多沃兹进行绩效损失。
Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. However, training belief trackers often requires expensive turn-level annotations of every user utterance. In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning. We propose a probabilistic dialog model, called the LAtent BElief State (LABES) model, where belief states are represented as discrete latent variables and jointly modeled with system responses given user inputs. Such latent variable modeling enables us to develop semi-supervised learning under the principled variational learning framework. Furthermore, we introduce LABES-S2S, which is a copy-augmented Seq2Seq model instantiation of LABES. In supervised experiments, LABES-S2S obtains strong results on three benchmark datasets of different scales. In utilizing unlabeled dialog data, semi-supervised LABES-S2S significantly outperforms both supervised-only and semi-supervised baselines. Remarkably, we can reduce the annotation demands to 50% without performance loss on MultiWOZ.
