Hierarchical Character Tagger for Short Text Spelling Error Correction

• URL: http://arxiv.org/abs/2109.14259v1
• Date: Wed, 29 Sep 2021 08:04:34 GMT
• Title: Hierarchical Character Tagger for Short Text Spelling Error Correction
• Authors: Mengyi Gao, Canran Xu, Peng Shi
• Abstract summary: We present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.
• Score: 27.187562419222218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art approaches to spelling error correction problem include Transformer-based Seq2Seq models, which require large training sets and suffer from slow inference time; and sequence labeling models based on Transformer encoders like BERT, which involve token-level label space and therefore a large pre-defined vocabulary dictionary. In this paper we present a Hierarchical Character Tagger model, or HCTagger, for short text spelling error correction. We use a pre-trained language model at the character level as a text encoder, and then predict character-level edits to transform the original text into its error-free form with a much smaller label space. For decoding, we propose a hierarchical multi-task approach to alleviate the issue of long-tail label distribution without introducing extra model parameters. Experiments on two public misspelling correction datasets demonstrate that HCTagger is an accurate and much faster approach than many existing models.

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