Text sentiment analysis is an important natural language processing (NLP) task and has received considerable attention in recent years. Numerous deep-learning based methods have been proposed in previous literature in terms of new deep neural networks (DNN) including new embedding strategies, new attention mechanisms, and new encoding layers. In this study, an alternative technical path is investigated to further improve the state-of-the-art performance of text sentiment analysis. An new effective learning framework is proposed that combines knowledge distillation and sample selection. A dually-born-again network (DBAN) is presented in which the teacher network and the student network are simultaneously trained through an iterative approach. A selection gate is defined to deal with training samples which are useless or even harmful for model training. Moreover, both the DBAN and sample selection are further improved by ensemble. The proposed framework can improve the existing state-of-the-art DNN models in sentiment analysis. Experimental results indicate that the proposed framework enhances the performances of existing networks. In addition, DBAN outperforms existing born-again network.

中文翻译：

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通过双重出生网络和样本选择进行情感分析

文本情感分析是一项重要的自然语言处理（NLP）任务，近年来受到了相当大的关注。在以前的文献中，已经针对新的深度神经网络（DNN）提出了许多基于深度学习的方法，包括新的嵌入策略，新的注意力机制和新的编码层。在这项研究中，研究了另一种技术途径，以进一步提高文本情感分析的最新水平。提出了一种新的有效学习框架，该框架结合了知识提炼和样本选择。提出了双重出生网络（DBAN），其中通过迭代方法同时训练教师网络和学生网络。定义了一个选择门来处理对模型训练无用甚至有害的训练样本。而且，通过集成进一步改善了DBAN和样本选择。所提出的框架可以改善情绪分析中现有的最新DNN模型。实验结果表明，所提出的框架增强了现有网络的性能。此外，DBAN的性能优于现有的重生网络。