一种用于供电服务评估的多模态多任务框架

应用神经网络进行电力服务中的语音情感分析和文本诉求分类是一种新颖的算法。相比于传统的方法，它避免了特征工程的做法，不需要人为进行特征选择，同时可以学习到更鲁棒的特征。受多任务学习的启发，本文设计一种多模态多任务的模型，可对语音和文本2种不同模态的数据进行处理，一方面使用情感分析来进行供电服务评估，另一方面引入用户诉求分类，用相似的任务提高单一任务的性能。实验表明，本文模型在单个任务上的结果与目前最好的模型结果接近。

A Multi-modal and Multi-task Framework for Power Supply Service Evaluation

The application of neural network into speech emotion analysis and text appeal classification in power service is a novel algorithm. Compared with the traditional method, the method of feature engineering is avoided, no human feature selection is needed, and more robust features can be learned. Inspired by the multi-task learning, this paper designs a multi-modal multi-task model, which can manage two different modes data of voice and text, on the one hand, it uses emotional analysis to evaluate the power supplying service, on the other hand, it introduces the classification of user demands, using similar tasks to improve the performance of a single task. The experiments show that the result in the single task of the model in this paper is close to the result of the best model.