论文标题
多元网络：在对话中进行多模式情感检测和情感分析的上下文意识到RNN
Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation
论文作者
论文摘要
在对话中的情感分析和情感检测是几种现实世界应用中的关键，其模式的增加有助于更好地理解潜在的情绪。多模式的情绪检测和情感分析可能特别有用，因为根据可用数据，应用程序将能够使用可用模式的特定子集。当前处理多模式功能的系统无法利用和捕获 - 通过各种方式，听众和说话者情感状态之间的依赖性以及可用方式之间的相关性和关系。在本文中，我们提出了一个端到端的RNN体系结构，该体系结构试图考虑所有提到的缺点。在撰写本文时，我们提出的模型在各种准确性和回归指标上都超过了基准数据集上的最新技术。
Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful, as applications will be able to use specific subsets of available modalities, as per the available data. Current systems dealing with Multi-modal functionality fail to leverage and capture - the context of the conversation through all modalities, the dependency between the listener(s) and speaker emotional states, and the relevance and relationship between the available modalities. In this paper, we propose an end to end RNN architecture that attempts to take into account all the mentioned drawbacks. Our proposed model, at the time of writing, out-performs the state of the art on a benchmark dataset on a variety of accuracy and regression metrics.
