The structured representation for semantic parsing in task-oriented assistant
systems is geared towards simple understanding of one-turn queries. Due to the
limitations of the representation, the session-based properties such as
co-reference resolution and context carryover are processed downstream in a
pipelined system. In this paper, we propose a semantic representation for such
task-oriented conversational systems that can represent concepts such as
co-reference and context carryover, enabling comprehensive understanding of
queries in a session. We release a new session-based, compositional
task-oriented parsing dataset of 20k sessions consisting of 60k utterances.
Unlike Dialog State Tracking Challenges, the queries in the dataset have
compositional forms. We propose a new family of Seq2Seq models for the
session-based parsing above, which achieve better or comparable performance to
the current state-of-the-art on ATIS, SNIPS, TOP and DSTC2. Notably, we improve
the best known results on DSTC2 by up to 5 points for slot-carryover.

本论文提出了一种适用于面向任务的对话系统的语义表示形式，该表示形式可以表示诸如共参照和上下文传递等概念，实现了对话过程中的全面理解。此外，更提出了一种新的 Seq2Seq 模型用于基于会话的解析，并在 ATIS、SNIPS、TOP 和 DSTC2 等数据集上获得了更好或相当的性能。

对话式语义解析

Conversational Semantic Parsing

The recent success of large pre-trained language models such as BERT and
GPT-2 has suggested the effectiveness of incorporating language priors in
downstream dialog generation tasks. However, the performance of pre-trained
models on the dialog task is not as optimal as expected. In this paper, we
propose a Pre-trained Role Alternating Language model (PRAL), designed
specifically for task-oriented conversational systems. We adopted (Wu et al.,
2019) that models two speakers separately. We also design several techniques,
such as start position randomization, knowledge distillation, and history
discount to improve pre-training performance. We introduce a task-oriented
dialog pretraining dataset by cleaning 13 existing data sets. We test PRAL on
three different downstream tasks. The results show that PRAL performs better or
on par with state-of-the-art methods.

本文提出一种专门为特定任务对话系统设计的预训练模型 PRAL，并设计采用多种技术来提高模型性能及引入了对话预训练数据集，实验结果表明 PRAL 在三种不同任务中表现优异，超越或与最先进的方法持平。