Semi-supervised Multitask Learning for Sequence Labeling
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.
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Tasks
ChunkingGrammatical Error DetectionLanguage ModelingLanguage ModellingNamed Entity RecognitionNamed Entity Recognition (NER)Part-Of-Speech TaggingPOSPOS TaggingSimilar Papers 제목 키워드 기반
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