Generating appropriate emotions for responses is essential for dialog systems
to provide human-like interaction in various application scenarios. Most
previous dialog systems tried to achieve this goal by learning empathetic
manners from anonymous conversational data. However, emotional responses
generated by those methods may be inconsistent, which will decrease user
engagement and service quality. Psychological findings suggest that the
emotional expressions of humans are rooted in personality traits. Therefore, we
propose a new task, Personality-affected Emotion Generation, to generate
emotion based on the personality given to the dialog system and further
investigate a solution through the personality-affected mood transition.
Specifically, we first construct a daily dialog dataset, Personality
EmotionLines Dataset (PELD), with emotion and personality annotations.
Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously
integrating personality and emotional factors and (2) extracting
multi-granularity emotional information in the dialog context. Finally, we
propose to model the personality as the transition weight by simulating the
mood transition process in the dialog system and solve the challenges above. We
conduct extensive experiments on PELD for evaluation. Results suggest that by
adopting our method, the emotion generation performance is improved by 13% in
macro-F1 and 5% in weighted-F1 from the BERT-base model.

使用人格特征为对话系统生成情感，通过模拟情绪转变过程来改善情感生成性能。

对话系统中受人格影响的情感生成

Personality-affected Emotion Generation in Dialog Systems

The COVID-19 pandemic is having a dramatic impact on societies and economies
around the world. With various measures of lockdowns and social distancing in
place, it becomes important to understand emotional responses on a large scale.
In this paper, we present the first ground truth dataset of emotional responses
to COVID-19. We asked participants to indicate their emotions and express these
in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500
short + 2,500 long texts). Our analyses suggest that emotional responses
correlated with linguistic measures. Topic modeling further revealed that
people in the UK worry about their family and the economic situation.
Tweet-sized texts functioned as a call for solidarity, while longer texts shed
light on worries and concerns. Using predictive modeling approaches, we were
able to approximate the emotional responses of participants from text within
14% of their actual value. We encourage others to use the dataset and improve
how we can use automated methods to learn about emotional responses and worries
about an urgent problem.

本研究提供了第一个 COVID-19 情感反应的真实数据集，并分析了语言模式与情感响应的相关性，其中英国人担心家庭和经济状况，短文本呼吁团结，而长文本更详细地阐述了担忧和关切。通过预测建模方法，我们能够在 14％的准确率内预测参与者的情感反应，鼓励其他人使用此数据集并改进自动化方法以了解紧迫问题的情感反应和担忧。

COVID-19 真实世界担忧数据集中的情绪测量

Measuring Emotions in the COVID-19 Real World Worry Dataset

Previous research on empathetic dialogue systems has mostly focused on
generating responses given certain emotions. However, being empathetic not only
requires the ability of generating emotional responses, but more importantly,
requires the understanding of user emotions and replying appropriately. In this
paper, we propose a novel end-to-end approach for modeling empathy in dialogue
systems: Mixture of Empathetic Listeners (MoEL). Our model first captures the
user emotions and outputs an emotion distribution. Based on this, MoEL will
softly combine the output states of the appropriate Listener(s), which are each
optimized to react to certain emotions, and generate an empathetic response.
Human evaluations on empathetic-dialogues (Rashkin et al., 2018) dataset
confirm that MoEL outperforms multitask training baseline in terms of empathy,
relevance, and fluency. Furthermore, the case study on generated responses of
different Listeners shows high interpretability of our model.

本研究提出一种新的端到端的对话系统建模方法 --MoEL，能够捕捉用户情感并回复相应的情感，经人类评估显示 MoEL 在共情、相关性和流畅性方面优于多任务训练基线，并且不同监听器生成的响应的案例研究显示我们的模型具有很高的解释性。

MoEL: 共情听众混合物

MoEL: Mixture of Empathetic Listeners

The majority of current systems for end-to-end dialog generation focus on
response quality without an explicit control over the affective content of the
responses. In this paper, we present an affect-driven dialog system, which
generates emotional responses in a controlled manner using a continuous
representation of emotions. The system achieves this by modeling emotions at a
word and sequence level using: (1) a vector representation of the desired
emotion, (2) an affect regularizer, which penalizes neutral words, and (3) an
affect sampling method, which forces the neural network to generate diverse
words that are emotionally relevant. During inference, we use a reranking
procedure that aims to extract the most emotionally relevant responses using a
human-in-the-loop optimization process. We study the performance of our system
in terms of both quantitative (BLEU score and response diversity), and
qualitative (emotional appropriateness) measures.

本文介绍了一种能够控制情感内容的情感驱动对话系统的方法，通过使用连续的情感表示来模拟词和句子级别的情感，并在推理过程中使用重新排名程序来获取最具情感相关性的响应。