XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers

Roshan Sharma; Bhiksha Raj

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arXiv · 2210.16643

XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers

Roshan Sharma·Bhiksha Raj

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Abstract

Transformers are among the state of the art for many tasks in speech, vision, and natural language processing, among others. Self-attentions, which are crucial contributors to this performance have quadratic computational complexity, which makes training on longer input sequences challenging. Prior work has produced state-of-the-art transformer variants with linear attention, however, current models sacrifice performance to achieve efficient implementations. In this work, we develop a novel linear transformer by examining the properties of the key-query product within self-attentions. Our model outperforms state of the art approaches on speech recognition and speech summarization, resulting in 1 % absolute WER improvement on the Librispeech-100 speech recognition benchmark and a new INTERVIEW speech recognition benchmark, and 5 points on ROUGE for summarization with How2.

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Roshan Sharma, Bhiksha Raj. 2022-10-29. XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers. https://arxiv.org/abs/2210.16643

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