Task-oriented dialogue (TOD) systems aim to achieve specific goals through
interactive dialogue. Such tasks usually involve following specific workflows,
i.e. executing a sequence of actions in a particular order. While prior work
has focused on supervised learning methods to condition on past actions, they
do not explicitly optimize for compliance to a desired workflow. In this paper,
we propose a novel framework based on reinforcement learning (RL) to generate
dialogue responses that are aligned with a given workflow. Our framework
consists of ComplianceScorer, a metric designed to evaluate how well a
generated response executes the specified action, combined with an RL
opimization process that utilizes an interactive sampling technique. We
evaluate our approach on two TOD datasets, Action-Based Conversations Dataset
(ABCD) (Chen et al., 2021a) and MultiWOZ 2.2 (Zang et al., 2020) on a range of
automated and human evaluation metrics. Our findings indicate that our RL-based
framework outperforms baselines and is effective at enerating responses that
both comply with the intended workflows while being expressed in a natural and
fluent manner.

本文提出了一种基于强化学习的新框架，用于生成与给定工作流程对齐的对话回复，通过结合 ComplianceScorer 评估指标和交互式采样技术的 RL 优化过程，评估结果表明该框架在任务导向对话系统上优于基线方法，并能生成遵循预定工作流程且自然流畅的对话回复。

基于工作流的任务导向对话生成

Workflow-Guided Response Generation for Task-Oriented Dialogue

This paper introduces a simple yet effective data-centric approach for the
task of improving persona-conditioned dialogue agents. Prior model-centric
approaches unquestioningly depend on the raw crowdsourced benchmark datasets
such as Persona-Chat. In contrast, we aim to fix annotation artifacts in
benchmarking, which is orthogonally applicable to any dialogue model.
Specifically, we augment relevant personas to improve dialogue dataset/agent,
by leveraging the primal-dual structure of the two tasks, predicting dialogue
responses and personas based on each other. Experiments on Persona-Chat show
that our approach outperforms pre-trained LMs by an 11.7 point gain in terms of
accuracy.

本文介绍了一种简单而有效的数据中心方法，用于改善个性化对话代理。通过利用针对两个任务的原始 - 对偶结构（预测对话响应和个人资料之间的联系），我们增强了相关人物角色，以改进对话数据集 / 代理，并修复了基准数据集的注释问题，其巨大地增加了模型的精度，体现在 Persona-Chat 上的实验中，我们的方法在准确性上比预训练 LM 高出 11.7 个百分点。

改进角色感知对话数据集的双重任务框架

Dual Task Framework for Improving Persona-grounded Dialogue Dataset

Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. ADEM(Lowe et al. 2017) formulated the
automatic evaluation of dialogue systems as a learning problem and showed that
such a model was able to predict responses which correlate significantly with
human judgements, both at utterance and system level. Their system was shown to
have beaten word-overlap metrics such as BLEU with large margins. We start with
the question of whether an adversary can game the ADEM model. We design a
battery of targeted attacks at the neural network based ADEM evaluation system
and show that automatic evaluation of dialogue systems still has a long way to
go. ADEM can get confused with a variation as simple as reversing the word
order in the text! We report experiments on several such adversarial scenarios
that draw out counterintuitive scores on the dialogue responses. We take a
systematic look at the scoring function proposed by ADEM and connect it to
linear system theory to predict the shortcomings evident in the system. We also
devise an attack that can fool such a system to rate a response generation
system as favorable. Finally, we allude to future research directions of using
the adversarial attacks to design a truly automated dialogue evaluation system.

本文讨论了如何利用对抗性攻击来操纵 ADEM（Automatic Dialogue Evaluation Metric）模型，实现人工智能的对话生成系统的自动化评价。