Messages in human conversations inherently convey emotions. The task of
detecting emotions in textual conversations leads to a wide range of
applications such as opinion mining in social networks. However, enabling
machines to analyze emotions in conversations is challenging, partly because
humans often rely on the context and commonsense knowledge to express emotions.
In this paper, we address these challenges by proposing a Knowledge-Enriched
Transformer (KET), where contextual utterances are interpreted using
hierarchical self-attention and external commonsense knowledge is dynamically
leveraged using a context-aware affective graph attention mechanism.
Experiments on multiple textual conversation datasets demonstrate that both
context and commonsense knowledge are consistently beneficial to the emotion
detection performance. In addition, the experimental results show that our KET
model outperforms the state-of-the-art models on most of the tested datasets in
F1 score.

本文提出了一种知识增强型 Transformer (KET) 模型，结合分层自注意力机制和上下文感知情感图注意力机制，成功解决了分析对话中情绪所面临的常识和上下文理解问题，实验结果表明 KET 模型在情感检测方面表现良好，F1-score 明显高于当前最先进的模型。

文本对话情感检测的知识增强 Transformer

Knowledge-Enriched Transformer for Emotion Detection in Textual  Conversations

Emotions are physiological states generated in humans in reaction to internal
or external events. They are complex and studied across numerous fields
including computer science. As humans, on reading "Why don't you ever text me!"
we can either interpret it as a sad or angry emotion and the same ambiguity
exists for machines. Lack of facial expressions and voice modulations make
detecting emotions from text a challenging problem. However, as humans
increasingly communicate using text messaging applications, and digital agents
gain popularity in our society, it is essential that these digital agents are
emotion aware, and respond accordingly.
In this paper, we propose a novel approach to detect emotions like happy, sad
or angry in textual conversations using an LSTM based Deep Learning model. Our
approach consists of semi-automated techniques to gather training data for our
model. We exploit advantages of semantic and sentiment based embeddings and
propose a solution combining both. Our work is evaluated on real-world
conversations and significantly outperforms traditional Machine Learning
baselines as well as other off-the-shelf Deep Learning models.

本篇论文提出利用基于 LSTM 的深度学习模型来检测文本对话中的情绪，讨论了利用语义和情感嵌入的半自动化技术来收集训练数据以及结合方案，该方法在真实场景下的表现优于传统机器学习基准线和其他现成的深度学习模型。