Neural Chat Translation (NCT) aims to translate conversational text between
speakers of different languages. Despite the promising performance of
sentence-level and context-aware neural machine translation models, there still
remain limitations in current NCT models because the inherent dialogue
characteristics of chat, such as dialogue coherence and speaker personality,
are neglected. In this paper, we propose to promote the chat translation by
introducing the modeling of dialogue characteristics into the NCT model. To
this end, we design four auxiliary tasks including monolingual response
generation, cross-lingual response generation, next utterance discrimination,
and speaker identification. Together with the main chat translation task, we
optimize the NCT model through the training objectives of all these tasks. By
this means, the NCT model can be enhanced by capturing the inherent dialogue
characteristics, thus generating more coherent and speaker-relevant
translations. Comprehensive experiments on four language directions
(English-German and English-Chinese) verify the effectiveness and superiority
of the proposed approach.

本文针对当前在聊天翻译模型中存在的限制提出了一种新的方法 —— 将对话特征建模引入到模型中。为此，设计了四个辅助任务，包括同语种响应生成、跨语种响应生成、下一个话语辨别和说话人识别。综合实验结果显示，这种方法实现了更多一致且更有说服力的翻译。

利用对话特征的最新神经聊天翻译技术

Towards Making the Most of Dialogue Characteristics for Neural Chat  Translation

Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.

研究发现，人机面对面互动过程中的社交信号、语言特征、情感表达等特征与用户对机器人的印象有关；其中，用户面部表情的快乐程度与机器人的好感度强相关，而对话特征与机器人的智能感知有关。这些特征有望成为在线补偿信号，用于基于增强学习的自适应人机对话系统中。