Related papers: Incorporating Residual and Normalization Layers into Analysis of Masked Language Models

Incorporating Residual and Normalization Layers into Analysis of Masked Language Models

URL: http://arxiv.org/abs/2109.07152v1
Date: Wed, 15 Sep 2021 08:32:20 GMT
Title: Incorporating Residual and Normalization Layers into Analysis of Masked Language Models
Authors: Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui
Abstract summary: We extend the scope of the analysis of Transformers from solely the attention patterns to the whole attention block. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed.
Score: 29.828669678974983
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Transformer architecture has become ubiquitous in the natural language processing field. To interpret the Transformer-based models, their attention patterns have been extensively analyzed. However, the Transformer architecture is not only composed of the multi-head attention; other components can also contribute to Transformers' progressive performance. In this study, we extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. Our analysis of Transformer-based masked language models shows that the token-to-token interaction performed via attention has less impact on the intermediate representations than previously assumed. These results provide new intuitive explanations of existing reports; for example, discarding the learned attention patterns tends not to adversely affect the performance. The codes of our experiments are publicly available.