Learning Interpretable and Discrete Representations with Adversarial Training for Unsupervised Text Classification
Learning continuous representations from unlabeled textual data has been increasingly studied for benefiting semi-supervised learning. Although it is relatively easier to interpret discrete representations, due to the difficulty of training, learning discrete representations for unlabeled textual data has not been widely explored. This work proposes TIGAN that learns to encode texts into two disentangled representations, including a discrete code and a continuous noise, where the discrete code represents interpretable topics, and the noise controls the variance within the topics. The discrete code learned by TIGAN can be used for unsupervised text classification. Compared to other unsupervised baselines, the proposed TIGAN achieves superior performance on six different corpora. Also, the performance is on par with a recently proposed weakly-supervised text classification method. The extracted topical words for representing latent topics show that TIGAN learns coherent and highly interpretable topics.
Code (0)
등록된 구현이 없습니다.
Tasks
General Classificationtext-classificationText ClassificationUnsupervised Text ClassificationSimilar Papers 제목 키워드 기반
InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood
Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold. In this paper, we propose a novel representation learning algorithm w…
Representation LearningUnsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hin…
DecoderDialogue GenerationDialogue InterpretationRepresentation Learning+3Learning Disentangled Joint Continuous and Discrete Representations
We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent distribution of variational autoencoders w…
TopicGAN: Unsupervised Text Generation from Explainable Latent Topics
Learning discrete representations of data and then generating data from the discovered representations have been increasingly studied because the obtained discrete representations can benefit unsupervised learning. Howev…
Image GenerationText GenerationLearning Discrete Structured Representations by Adversarially Maximizing Mutual Information
We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable. Calculating mutual information is intractable i…