Attending to Characters in Neural Sequence Labeling Models

Marek Rei; Gamal K. O. Crichton; Sampo Pyysalo

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arXiv · 1611.04361

Attending to Characters in Neural Sequence Labeling Models

Marek Rei·Gamal K. O. Crichton·Sampo Pyysalo

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Abstract

Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining alternative word representations. By using an attention mechanism, the model is able to dynamically decide how much information to use from a word- or character-level component. We evaluated different architectures on a range of sequence labeling datasets, and character-level extensions were found to improve performance on every benchmark. In addition, the proposed attention-based architecture delivered the best results even with a smaller number of trainable parameters.

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Marek Rei, Gamal K. O. Crichton, Sampo Pyysalo. 2016-11-14. Attending to Characters in Neural Sequence Labeling Models. https://arxiv.org/abs/1611.04361

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