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    <abstract>Multilingual understanding models (or encoder-based), pre-trained via masked language modeling, have achieved promising results on many language understanding tasks (e.g., mBERT). However, these models are not capable of generating high-quality text compared with decoder-based causal language models. Can we transform a pre-trained language understanding model into an effective language generation model? We propose a Semantic-Guided Alignment-then-Denoising (SGA) approach to adapt a multilingual encoder to a multilingual generator with a small number of additional parameters. Experiments show that the proposed approach is an effective adaption method, outperforming widely-used initialization-based methods with gains of 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation on XLM-R_large. On the other hand, we observe that XLM-R is still inferior to mBART in supervised settings despite better results on zero-shot settings, indicating that more exploration is required to make understanding models strong generators. Our code is available at https://github.com/chengzhipanpan/XLMR4MT.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.1031</identifier>
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%0 Conference Proceedings
%T Extrapolating Multilingual Understanding Models as Multilingual Generators
%A Wu, Bohong
%A Yuan, Fei
%A Zhao, Hai
%A Li, Lei
%A Xu, Jingjing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wu-etal-2023-extrapolating
%X Multilingual understanding models (or encoder-based), pre-trained via masked language modeling, have achieved promising results on many language understanding tasks (e.g., mBERT). However, these models are not capable of generating high-quality text compared with decoder-based causal language models. Can we transform a pre-trained language understanding model into an effective language generation model? We propose a Semantic-Guided Alignment-then-Denoising (SGA) approach to adapt a multilingual encoder to a multilingual generator with a small number of additional parameters. Experiments show that the proposed approach is an effective adaption method, outperforming widely-used initialization-based methods with gains of 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation on XLM-R_large. On the other hand, we observe that XLM-R is still inferior to mBART in supervised settings despite better results on zero-shot settings, indicating that more exploration is required to make understanding models strong generators. Our code is available at https://github.com/chengzhipanpan/XLMR4MT.
%R 10.18653/v1/2023.findings-emnlp.1031
%U https://aclanthology.org/2023.findings-emnlp.1031
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1031
%P 15432-15444

Markdown (Informal)

[Extrapolating Multilingual Understanding Models as Multilingual Generators](https://aclanthology.org/2023.findings-emnlp.1031) (Wu et al., Findings 2023)

• Extrapolating Multilingual Understanding Models as Multilingual Generators (Wu et al., Findings 2023)

ACL

• Bohong Wu, Fei Yuan, Hai Zhao, Lei Li, and Jingjing Xu. 2023. Extrapolating Multilingual Understanding Models as Multilingual Generators. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15432–15444, Singapore. Association for Computational Linguistics.