近日，博士生马绘的研究成果被ELSEVIER旗下计算机top期刊Knowledge  Based System（KBS）录用，KBS为中科院一区期刊，CCF推荐期刊C类。

  题目：A multi-view network for real-time emotion recognition in conversations（基于多视角网络的对话实时情感识别）

  摘要：Real-time emotion recognition in conversations (RTERC), the task of using the historical context to identify the emotion of a query utterance in a conversation, is important for opinion mining and building empathetic machines. Existing works mainly focus on obtaining each utterance representation separately and then utilizing utterance-level features to model the emotion representation of the query. These approaches treat each utterance as a unit and capture the utterance-level dependencies in the context, but ignore the word-level dependencies among different utterances. In this paper, we propose a multi-view network (MVN) to explore the emotion representation of a query from two different views, i.e., word- and utterance-level views. For the word-level view, MVN takes the context and query as word sequences and then models the word-level dependencies among utterances. For the utterance-level view, MVN extracts each utterance representation separately and then models the utterance-level dependencies in the context. Experimental results on two public emotion conversation datasets show that the proposed model outperforms the state-of-the-art baselines.

  对话中的实时情感识别对于观点挖掘以及构建情感机器人非常重要。存在的模型主要关注于单独得到每个话语的表示， 然后利用话语级别的特征来建模被识别话语的情感表示。这些方法把每个话语作为一个单元，可以捕捉对话上文中话语级别的依赖性，但是忽略了不同话语间的词级别的依赖性。因此，本文提出一个多视角模型，该模型从词级别和话语级别这两个不同的视角来探索被识别话语的情感表示。