论文标题
BIERU：双向情感反复发作单元，用于对话情感分析
BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
论文作者
论文摘要
近年来，对对话中的情感分析引起了人们的关注，因为它可以使用的应用程序不断增加的应用，例如情感分析，推荐系统和人类机器人的相互作用。对话情感分析和单句话情感分析之间的主要区别在于存在上下文信息的存在，这可能会影响对话中话语的情感。但是，如何在对话中有效编码上下文信息仍然是一个挑战。现有方法采用复杂的深度学习结构来区分对话中的不同各方，然后对上下文信息进行建模。在本文中，我们提出了一个快速，紧凑和参数效率的党派命运框架，称为双向情感复发单元，用于对话情感分析。在我们的系统中，一个广义的神经张量块，然后是两通道分类器，分别旨在执行上下文组成性和情感分类。在三个标准数据集上进行的广泛实验表明，在大多数情况下，我们的模型优于最新技术。
Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information which may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases.
