Hierarchical Attention Transformer Architecture For Syntactic Spell Correction

• URL: http://arxiv.org/abs/2005.04876v1
• Date: Mon, 11 May 2020 06:19:01 GMT
• Title: Hierarchical Attention Transformer Architecture For Syntactic Spell Correction
• Authors: Abhishek Niranjan, M Ali Basha Shaik, Kushal Verma
• Abstract summary: We propose multi encoder-single decoder variation of conventional transformer. We report significant improvement of 0.11%, 0.32% and 0.69% in character (CER), word (WER) and sentence (SER) error rates. Our architecture is also trains 7.8 times faster, and is only about 1/3 in size from the next most accurate model.
• Score: 1.0312968200748118
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The attention mechanisms are playing a boosting role in advancements in sequence-to-sequence problems. Transformer architecture achieved new state of the art results in machine translation, and it's variants are since being introduced in several other sequence-to-sequence problems. Problems which involve a shared vocabulary, can benefit from the similar semantic and syntactic structure in the source and target sentences. With the motivation of building a reliable and fast post-processing textual module to assist all the text-related use cases in mobile phones, we take on the popular spell correction problem. In this paper, we propose multi encoder-single decoder variation of conventional transformer. Outputs from the three encoders with character level 1-gram, 2-grams and 3-grams inputs are attended in hierarchical fashion in the decoder. The context vectors from the encoders clubbed with self-attention amplify the n-gram properties at the character level and helps in accurate decoding. We demonstrate our model on spell correction dataset from Samsung Research, and report significant improvement of 0.11\%, 0.32\% and 0.69\% in character (CER), word (WER) and sentence (SER) error rates from existing state-of-the-art machine-translation architectures. Our architecture is also trains ~7.8 times faster, and is only about 1/3 in size from the next most accurate model.

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