  Task-oriented dialog systems have witnessed substantial progress due to
conversational pre-training techniques. Yet, two significant challenges
persist. First, most systems primarily utilize the latest turn's state label
for the generator. This practice overlooks the comprehensive value of state
labels in boosting the model's understanding for future generations. Second, an
overreliance on generated policy often leads to error accumulation, resulting
in suboptimal responses when adhering to incorrect actions. To combat these
challenges, we propose turn-level multi-task objectives for the encoder. With
the guidance of essential information from labeled intermediate states, we
establish a more robust representation for both understanding and generation.
For the decoder, we introduce an action tree-based scheduled sampling
technique. Specifically, we model the hierarchical policy as trees and utilize
the similarity between trees to sample negative policy based on scheduled
sampling, hoping the model to generate invariant responses under perturbations.
This method simulates potential pitfalls by sampling similar negative policy,
bridging the gap between task-oriented dialog training and inference. Among
methods without continual pre-training, our approach achieved state-of-the-art
(SOTA) performance on the MultiWOZ dataset series and was also competitive with
pre-trained SOTA methods.
