In this paper, we study the task of selecting the optimal response given a
user and system utterance history in retrieval-based multi-turn dialog systems.
Recently, pre-trained language models (e.g., BERT, RoBERTa, and ELECTRA) showed
significant improvements in various natural language processing tasks. This and
similar response selection tasks can also be solved using such language models
by formulating the tasks as dialog--response binary classification tasks.
Although existing works using this approach successfully obtained
state-of-the-art results, we observe that language models trained in this
manner tend to make predictions based on the relatedness of history and
candidates, ignoring the sequential nature of multi-turn dialog systems. This
suggests that the response selection task alone is insufficient for learning
temporal dependencies between utterances. To this end, we propose utterance
manipulation strategies (UMS) to address this problem. Specifically, UMS
consist of several strategies (i.e., insertion, deletion, and search), which
aid the response selection model towards maintaining dialog coherence. Further,
UMS are self-supervised methods that do not require additional annotation and
thus can be easily incorporated into existing approaches. Extensive evaluation
across multiple languages and models shows that UMS are highly effective in
teaching dialog consistency, which leads to models pushing the state-of-the-art
with significant margins on multiple public benchmark datasets.

本文研究了在检索型的多轮对话系统中，如何在给定用户和系统对话历史记录的情况下选择最优回复。本研究发现，利用预训练的语言模型可以解决此类回复选择任务，但这种方法只考虑了历史和候选回复的相关性，而忽略了多轮对话系统的时序性。因此，作者提出了一种称为 utterance manipulation strategies (UMS) 的方法，该方法通过插入、删除和搜索等策略来保持对话一致性。研究结果表明，UMS 是高效的自我监督方法，且可以在多种公共基准测试数据集上推动现有技术的发展。

多轮响应选择的话语操纵策略

Do Response Selection Models Really Know What's Next? Utterance  Manipulation Strategies for Multi-turn Response Selection

Building open domain conversational systems that allow users to have engaging
conversations on topics of their choice is a challenging task. Alexa Prize was
launched in 2016 to tackle the problem of achieving natural, sustained,
coherent and engaging open-domain dialogs. In the second iteration of the
competition in 2018, university teams advanced the state of the art by using
context in dialog models, leveraging knowledge graphs for language
understanding, handling complex utterances, building statistical and
hierarchical dialog managers, and leveraging model-driven signals from user
responses. The 2018 competition also included the provision of a suite of tools
and models to the competitors including the CoBot (conversational bot) toolkit,
topic and dialog act detection models, conversation evaluators, and a sensitive
content detection model so that the competing teams could focus on building
knowledge-rich, coherent and engaging multi-turn dialog systems. This paper
outlines the advances developed by the university teams as well as the Alexa
Prize team to achieve the common goal of advancing the science of
Conversational AI. We address several key open-ended problems such as
conversational speech recognition, open domain natural language understanding,
commonsense reasoning, statistical dialog management, and dialog evaluation.
These collaborative efforts have driven improved experiences by Alexa users to
an average rating of 3.61, the median duration of 2 mins 18 seconds, and
average turns to 14.6, increases of 14%, 92%, 54% respectively since the launch
of the 2018 competition. For conversational speech recognition, we have
improved our relative Word Error Rate by 55% and our relative Entity Error Rate
by 34% since the launch of the Alexa Prize. Socialbots improved in quality
significantly more rapidly in 2018, in part due to the release of the CoBot
toolkit.

2018 年的 Alexa Prize 竞赛促进了会话人工智能的发展，多所大学加强了会话模型的上下文理解和对大量不同类型输入的处理，并借助知识图谱等工具提高了自然语言理解能力，使用统计和分层对话管理和模型驱动的用户响应机制，同时提供 CoBot 工具包、话题检测模型、对话行为检测模型、对话评估等套件，构建了知识丰富、连贯、吸引人的多轮对话系统。