In this paper, we propose an end-to-end sentiment-aware conversational agent
based on two models: a reply sentiment prediction model, which leverages the
context of the dialogue to predict an appropriate sentiment for the agent to
express in its reply; and a text generation model, which is conditioned on the
predicted sentiment and the context of the dialogue, to produce a reply that is
both context and sentiment appropriate. Additionally, we propose to use a
sentiment classification model to evaluate the sentiment expressed by the agent
during the development of the model. This allows us to evaluate the agent in an
automatic way. Both automatic and human evaluation results show that explicitly
guiding the text generation model with a pre-defined set of sentences leads to
clear improvements, both regarding the expressed sentiment and the quality of
the generated text.

本文提出了一种以情感为中心的端到端会话代理，基于两种模型，回复情感预测模型和文本生成模型，并且利用情感分类模型来评估代理的表达情感。通过指导预定义一组句子的文本生成模型，有效提升了生成文本的质量和表达情感的准确度，通过人工及自动化评估显示了显著的改进。