Semi-supervised Multitask Learning for Sequence Labeling

Marek Rei

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arXiv · 1704.07156

Semi-supervised Multitask Learning for Sequence Labeling

Marek Rei

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Abstract

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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BibTeX RIS

Marek Rei. 2017-04-24. Semi-supervised Multitask Learning for Sequence Labeling. https://arxiv.org/abs/1704.07156

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