@inproceedings{soltan-etal-2023-recipes,
title = "Recipes for Sequential Pre-training of Multilingual Encoder and {S}eq2{S}eq Models",
author = "Soltan, Saleh and
Rosenbaum, Andy and
Falke, Tobias and
Lu, Qin and
Rumshisky, Anna and
Hamza, Wael",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.598",
doi = "10.18653/v1/2023.findings-acl.598",
pages = "9380--9394",
abstract = "Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each have advantages, however training both model types from scratch is computationally expensive. We explore recipes to improve pre-training efficiency by initializing one model from the other. (1) Extracting the encoder from a seq2seq model, we show it under-performs a Masked Language Modeling (MLM) encoder, particularly on sequence labeling tasks. Variations of masking during seq2seq training, reducing the decoder size, and continuing with a small amount of MLM training do not close the gap. (2) Conversely, using an encoder to warm-start seq2seq training, we show that by unfreezing the encoder partway through training, we can match task performance of a from-scratch seq2seq model. Overall, this two-stage approach is an efficient recipe to obtain both a multilingual encoder and a seq2seq model, matching the performance of training each model from scratch while reducing the total compute cost by 27{\%}.",
}
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%0 Conference Proceedings
%T Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models
%A Soltan, Saleh
%A Rosenbaum, Andy
%A Falke, Tobias
%A Lu, Qin
%A Rumshisky, Anna
%A Hamza, Wael
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F soltan-etal-2023-recipes
%X Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each have advantages, however training both model types from scratch is computationally expensive. We explore recipes to improve pre-training efficiency by initializing one model from the other. (1) Extracting the encoder from a seq2seq model, we show it under-performs a Masked Language Modeling (MLM) encoder, particularly on sequence labeling tasks. Variations of masking during seq2seq training, reducing the decoder size, and continuing with a small amount of MLM training do not close the gap. (2) Conversely, using an encoder to warm-start seq2seq training, we show that by unfreezing the encoder partway through training, we can match task performance of a from-scratch seq2seq model. Overall, this two-stage approach is an efficient recipe to obtain both a multilingual encoder and a seq2seq model, matching the performance of training each model from scratch while reducing the total compute cost by 27%.
%R 10.18653/v1/2023.findings-acl.598
%U https://aclanthology.org/2023.findings-acl.598
%U https://doi.org/10.18653/v1/2023.findings-acl.598
%P 9380-9394
Markdown (Informal)
[Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models](https://aclanthology.org/2023.findings-acl.598) (Soltan et al., Findings 2023)
ACL