While there have been significant advances in de-tecting emotions in text, in
the field of utter-ance-level emotion recognition (ULER), there are still many
problems to be solved. In this paper, we address some challenges in ULER in
dialog sys-tems. (1) The same utterance can deliver different emotions when it
is in different contexts or from different speakers. (2) Long-range contextual
in-formation is hard to effectively capture. (3) Unlike the traditional text
classification problem, this task is supported by a limited number of datasets,
among which most contain inadequate conversa-tions or speech. To address these
problems, we propose a hierarchical transformer framework (apart from the
description of other studies, the "transformer" in this paper usually refers to
the encoder part of the transformer) with a lower-level transformer to model
the word-level input and an upper-level transformer to capture the context of
utterance-level embeddings. We use a pretrained language model bidirectional
encoder representa-tions from transformers (BERT) as the lower-level
transformer, which is equivalent to introducing external data into the model
and solve the problem of data shortage to some extent. In addition, we add
speaker embeddings to the model for the first time, which enables our model to
capture the in-teraction between speakers. Experiments on three dialog emotion
datasets, Friends, EmotionPush, and EmoryNLP, demonstrate that our proposed
hierarchical transformer network models achieve 1.98%, 2.83%, and 3.94%
improvement, respec-tively, over the state-of-the-art methods on each dataset
in terms of macro-F1.

通过提出一种基于分层 Transformer 框架的情感识别方法，该方法结合了预训练语言模型和说话人嵌入，能在不同背景环境和不同说话人的情况下有效地捕捉言语的情感信息，实验结果表明该方法在三个对话数据集上分别取得了 1.98％、2.83％和 3.94％的 F1 宏平均分数提升。