近日，收到期刊《Neural Computing and Applications》的通知，博士生马绘的论文《HAN-ReGRU: hierarchical attention network with residual gated recurrent unit for emotion recognition in conversation》被该期刊录用，该期刊是中国计算机学会推荐C类国际期刊。本文研究主要关注于人机对话领域，提出一种层次注意力模型，用于解决人机对话中的情绪识别问题，论文摘要如下：
Emotion recognition in conversation aims to identify the emotion of each consistent utterance in a conversation from several pre-defined emotions. The task has recently become a new popular research frontier in natural language processing because of the increase in open conversational data and its application in opinion mining. However, most existing methods for the task cannot capture the long-range contextual information in an utterance and a conversation effectively. To alleviate this problem, we propose a novel hierarchical attention network with residual gated recurrent unit framework. Firstly, we adopt the pre-trained BERT-Large model to obtain context-dependent representation for each token of each utterance in a conversation. Then, a hierarchical attention network is proposed to capture long-range contextual information about the conversation structure. Besides, in order to better model position information of the utterances in a conversation, we add position embedding to the input of the multi-head attention. Experiments on two textual dialogue emotion datasets demonstrate that our model significantly outperforms the state-of-the-art baseline methods.