A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Multi-task learning leverages potential correlations among related tasks to extract common features and yield performance gains. However, most previous works only consider simple or weak interactions, thereby failing to model complex correlations among three or more tasks. In this paper, we propose a multi-task learning architecture with four types of recurrent neural layers to fuse information across multiple related tasks. The architecture is structurally flexible and considers various interactions among tasks, which can be regarded as a generalized case of many previous works. Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others.
Code (0)
등록된 구현이 없습니다.
Tasks
General ClassificationMulti-Task Learningtext-classificationText ClassificationSimilar Papers 제목 키워드 기반
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion
Data generated from real world events are usually temporal and contain multimodal information such as audio, visual, depth, sensor etc. which are required to be intelligently combined for classification tasks. In this pa…
Sequential Learning of Convolutional Features for Effective Text Classification
Text classification has been one of the major problems in natural language processing. With the advent of deep learning, convolutional neural network (CNN) has been a popular solution to this task. However, CNNs which we…
ClassificationGeneral ClassificationSentiment AnalysisText Categorization+2Combining Recurrent and Convolutional Neural Networks for Relation Classification
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different arc…
ClassificationGeneral ClassificationRelationRelation ClassificationLenia and Expanded Universe
We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kern…
Artificial LifeMulti-Prediction Deep Boltzmann Machines
We introduce the Multi-Prediction Deep Boltzmann Machine (MP-DBM). The MP-DBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a famil…
ClassificationGeneral ClassificationPrediction