MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators

• URL: http://arxiv.org/abs/2110.06609v1
• Date: Wed, 13 Oct 2021 10:06:21 GMT
• Title: MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
• Authors: Zhixing Tan, Xiangwen Zhang, Shuo Wang, Yang Liu
• Abstract summary: We present Multi-Stage Prompting, a simple and lightweight approach for better adapting pre-trained language models to translation tasks. To make pre-trained language models better translators, we divide the translation process via pre-trained language models into three separate stages. During each stage, we independently apply different continuous prompts for allowing pre-trained language models better adapting to translation tasks.
• Score: 10.557167523009392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained language models have recently been shown to be able to perform translation without finetuning via prompting. Inspired by these findings, we study improving the performance of pre-trained language models on translation tasks, where training neural machine translation models is the current de facto approach. We present Multi-Stage Prompting, a simple and lightweight approach for better adapting pre-trained language models to translation tasks. To make pre-trained language models better translators, we divide the translation process via pre-trained language models into three separate stages: the encoding stage, the re-encoding stage, and the decoding stage. During each stage, we independently apply different continuous prompts for allowing pre-trained language models better adapting to translation tasks. We conduct extensive experiments on low-, medium-, and high-resource translation tasks. Experiments show that our method can significantly improve the translation performance of pre-trained language models.

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