SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling
Multi-task learning (MTL) has been studied recently for sequence labeling. Typically, auxiliary tasks are selected specifically in order to improve the performance of a target task. Jointly learning multiple tasks in a way that benefit all of them simultaneously can increase the utility of MTL. In order to do so, we propose a new LSTM cell which contains both shared parameters that can learn from all tasks, and task-specific parameters that can learn task-specific information. We name it a Shared-Cell Long-Short Term Memory SC-LSTM. Experimental results on three sequence labeling benchmarks (named-entity recognition, text chunking, and part-of-speech tagging) demonstrate the effectiveness of our SC-LSTM cell.
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ChunkingMulti-Task Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Part-Of-Speech TaggingMethods 이 논문이 사용한 방법론
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