Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning

• URL: http://arxiv.org/abs/2004.08097v1
• Date: Fri, 17 Apr 2020 07:43:38 GMT
• Title: Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
• Authors: Joongbo Shin, Yoonhyung Lee, Seunghyun Yoon, Kyomin Jung
• Abstract summary: We propose a novel deep bidirectional language model called Transformer-based Text Autoencoder (T-TA) The T-TA computes contextual language representations without repetition and has benefits of the deep bidirectional architecture like BERT. In run-time experiments on CPU environments, the proposed T-TA performs over six times faster than the BERT-based model in the reranking task and twelve times faster in the semantic similarity task.
• Score: 31.897630023454067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Even though BERT achieves successful performance improvements in various supervised learning tasks, applying BERT for unsupervised tasks still holds a limitation that it requires repetitive inference for computing contextual language representations. To resolve the limitation, we propose a novel deep bidirectional language model called Transformer-based Text Autoencoder (T-TA). The T-TA computes contextual language representations without repetition and has benefits of the deep bidirectional architecture like BERT. In run-time experiments on CPU environments, the proposed T-TA performs over six times faster than the BERT-based model in the reranking task and twelve times faster in the semantic similarity task. Furthermore, the T-TA shows competitive or even better accuracies than those of BERT on the above tasks.

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