Dialogue systems need to deal with the unpredictability of user intents to
track dialogue state and the heterogeneity of slots to understand user
preferences. In this paper we investigate the hypothesis that solving these
challenges as one unified model will allow the transfer of parameter support
data across the different tasks. The proposed principled model is based on a
Transformer encoder, trained on multiple tasks, and leveraged by a rich input
that conditions the model on the target inferences. Conditioning the
Transformer encoder on multiple target inferences over the same corpus, i.e.,
intent and multiple slot types, allows learning richer language interactions
than a single-task model would be able to. In fact, experimental results
demonstrate that conditioning the model on an increasing number of dialogue
inference tasks leads to improved results: on the MultiWOZ dataset, the joint
intent and slot detection can be improved by 3.2\% by conditioning on intent,
10.8\% by conditioning on slot and 14.4\% by conditioning on both intent and
slots. Moreover, on real conversations with Farfetch costumers, the proposed
conditioned BERT can achieve high joint-goal and intent detection performance
throughout a dialogue.

通过在多个任务上训练基于 Transformer 编码器的统一模型，并借助富输入在目标推理上对模型进行条件化，本文探索了解决对话系统中用户意图的不可预测性和插槽的异构性的假设，并表明将模型条件化于对同一语料库上的多个推理任务，如意图和多个插槽类型，可以学习到比单任务模型更丰富的语言交互；实验结果表明，将模型条件化于越来越多的对话推理任务可以提高性能，MultiWOZ 数据集上，通过对意图进行条件化可以提高 3.2％的联合意图和插槽检测性能，通过对插槽进行条件化可以提高 10.8％的性能，同时对意图和插槽进行条件化可以提高 14.4％的性能；此外，在与 Farfetch 客户的实际对话中，该条件化 BERT 可以在整个对话过程中实现高效的联合目标和意图检测性能。