This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance while reducing the number of network parameters. In our proposed framework, DST operates on a set of candidate values for each slot that has been mentioned so far. These candidate sets are generated using LU slot annotations for the current user utterance, dialogue acts corresponding to the preceding system utterance and the dialogue state estimated for the previous turn, enabling DST to handle slots with a large or unbounded set of possible values and deal with slot values not seen during training. Furthermore, to bridge the gap between training and inference, we investigate the use of scheduled sampling on LU output for the current user utterance as well as the DST output for the preceding turn.

本文提出了一种新的多任务学习方法，旨在为面向任务的对话系统中的语言理解（LU）和对话状态跟踪（DST）进行学习。多任务训练使得编码用户话语的神经网络层用于LU和DST，通过减少网络参数并提高性能。 本研究提出了DST框架，用于考虑有大量或无限可能值的插槽及以往对话中未见过的槽值，并探讨了在LU和DST输出上使用计划采样来弥合训练和推理之间的差距。

基于多任务学习的联合语言理解和对话状态跟踪