In this paper, we study the problem of data augmentation for language
understanding in task-oriented dialogue system. In contrast to previous work
which augments an utterance without considering its relation with other
utterances, we propose a sequence-to-sequence generation based data
augmentation framework that leverages one utterance's same semantic
alternatives in the training data. A novel diversity rank is incorporated into
the utterance representation to make the model produce diverse utterances and
these diversely augmented utterances help to improve the language understanding
module. Experimental results on the Airline Travel Information System dataset
and a newly created semantic frame annotation on Stanford Multi-turn,
Multidomain Dialogue Dataset show that our framework achieves significant
improvements of 6.38 and 10.04 F-scores respectively when only a training set
of hundreds utterances is represented. Case studies also confirm that our
method generates diverse utterances.

本研究针对面向任务对话系统中语言理解的数据增强问题，提出了基于序列到序列生成的数据增强框架来提高模型的性能，该框架利用训练数据中某个话语的同义替代品来增强数据，将多样性排名纳入话语表示中，生成多样性增加的话语，实验证明在仅有数百个话语的情况下，在 Airline Travel Information System 数据集和 Stanford Multi-turn，Multidomain Dialogue 数据集上都取得了显著的 6.38 和 10.04 分数提高。

面向对话语言理解的序列到序列数据增强

Sequence-to-Sequence Data Augmentation for Dialogue Language  Understanding

In this paper, we present a neural network based task-oriented dialogue
system that can be optimized end-to-end with deep reinforcement learning (RL).
The system is able to track dialogue state, interface with knowledge bases, and
incorporate query results into agent's responses to successfully complete
task-oriented dialogues. Dialogue policy learning is conducted with a hybrid
supervised and deep RL methods. We first train the dialogue agent in a
supervised manner by learning directly from task-oriented dialogue corpora, and
further optimize it with deep RL during its interaction with users. In the
experiments on two different dialogue task domains, our model demonstrates
robust performance in tracking dialogue state and producing reasonable system
responses. We show that deep RL based optimization leads to significant
improvement on task success rate and reduction in dialogue length comparing to
supervised training model. We further show benefits of training task-oriented
dialogue model end-to-end comparing to component-wise optimization with
experiment results on dialogue simulations and human evaluations.

介绍了一种基于神经网络的任务导向对话系统，可以使用深度强化学习进行端到端优化，可跟踪对话状态，与知识库交互，并将查询结果整合到代理人的响应中，以成功完成任务导向对话。

基于深度强化学习的任务导向对话模型端到端优化

End-to-End Optimization of Task-Oriented Dialogue Model with Deep  Reinforcement Learning

A fundamental requirement of any task-oriented dialogue system is the ability
to generate object descriptions that refer to objects in the task domain. The
subproblem of content selection for object descriptions in task-oriented
dialogue has been the focus of much previous work and a large number of models
have been proposed. In this paper, we use the annotated COCONUT corpus of
task-oriented design dialogues to develop feature sets based on Dale and
Reiters (1995) incremental model, Brennan and Clarks (1996) conceptual pact
model, and Jordans (2000b) intentional influences model, and use these feature
sets in a machine learning experiment to automatically learn a model of content
selection for object descriptions. Since Dale and Reiters model requires a
representation of discourse structure, the corpus annotations are used to
derive a representation based on Grosz and Sidners (1986) theory of the
intentional structure of discourse, as well as two very simple representations
of discourse structure based purely on recency. We then apply the
rule-induction program RIPPER to train and test the content selection component
of an object description generator on a set of 393 object descriptions from the
corpus. To our knowledge, this is the first reported experiment of a trainable
content selection component for object description generation in dialogue.
Three separate content selection models that are based on the three theoretical
models, all independently achieve accuracies significantly above the majority
class baseline (17%) on unseen test data, with the intentional influences model
(42.4%) performing significantly better than either the incremental model
(30.4%) or the conceptual pact model (28.9%). But the best performing models
combine all the feature sets, achieving accuracies near 60%. Surprisingly, a
simple recency-based representation of discourse structure does as well as one
based on intentional structure. To our knowledge, this is also the first
empirical comparison of a representation of Grosz and Sidners model of
discourse structure with a simpler model for any generation task.

本文研究聊天机器人生成任务型对话中的对象描述内容选取这一子问题，使用基于不同模型的特征集进行机器学习并训练出选择对象描述内容的组件，其中包括递增模型、概念协定模型和意图影响模型。同时，本文还与简单的 recentity 模型进行了对比实验，发现意图影响模型表现最佳。