Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers

• URL: http://arxiv.org/abs/2101.00234v1
• Date: Fri, 1 Jan 2021 13:53:22 GMT
• Title: Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers
• Authors: Machel Reid, Edison Marrese-Taylor and Yutaka Matsuo
• Abstract summary: We develop the Subformer, a parameter efficient Transformer-based model. Experiments on machine translation, abstractive summarization, and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters.
• Score: 16.88840622945725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advent of the Transformer can arguably be described as a driving force behind many of the recent advances in natural language processing. However, despite their sizeable performance improvements, as recently shown, the model is severely over-parameterized, being parameter inefficient and computationally expensive to train. Inspired by the success of parameter-sharing in pretrained deep contextualized word representation encoders, we explore parameter-sharing methods in Transformers, with a specific focus on encoder-decoder models for sequence-to-sequence tasks such as neural machine translation. We perform an analysis of different parameter sharing/reduction methods and develop the Subformer, a parameter efficient Transformer-based model which combines the newly proposed Sandwich-style parameter sharing technique - designed to overcome the deficiencies in naive cross-layer parameter sharing for generative models - and self-attentive embedding factorization (SAFE). Experiments on machine translation, abstractive summarization, and language modeling show that the Subformer can outperform the Transformer even when using significantly fewer parameters.

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