Papers with adaption method

2 papers
Effective Fine-Tuning Methods for Cross-lingual Adaptation (2021.emnlp-main)

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Challenge: Large scale multilingual pre-trained language models have shown promising results in zero- and few-shot cross-lingual tasks.
Approach: They propose a co-tuning method that aims to learn more generalized semantic equivalences when the languages are structurally dissimilar.
Outcome: The proposed method improves on cross-lingual inference and review tasks by capturing the semantic relationship in the parallel data when a few translation pairs are available.
Extrapolating Multilingual Understanding Models as Multilingual Generators (2023.findings-emnlp)

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Challenge: Existing multilingual understanding models are not capable of generating high-quality text compared with decoder-based causal language models.
Approach: They propose a method to adapt a multilingual encoder to a language generator with a small number of additional parameters.
Outcome: The proposed approach outperforms initialization-based methods with 9.4 BLEU on machine translation, 8.1 Rouge-L on question generation, and 5.5 METEOR on story generation.

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