Papers with neural adapters

2 papers
Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer (2023.findings-emnlp)

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Challenge: Existing studies explore performing text style transfer on attributes like age, gender, formality, politeness, and formality.
Approach: They propose a framework that freezes the pre-trained model’s original parameters and enables the development of a multiple-attribute text style transfer model.
Outcome: The proposed model outperforms state-of-the-art models on sentiment transfer and multiple-attribute transfer tasks with significantly less computational resources.
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation (2023.acl-long)

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Challenge: Recent work in multimodal machine translation (MT) has shown that ambiguity can be resolved using accompanying context such as images.
Approach: They propose a multimodal machine translation approach based on a strong text-only MT model and a novel guided self-attention mechanism to train it.
Outcome: The proposed model outperforms existing models on EnglishFrench, EnglishGerman and EnglishCzech benchmarks and is freely available.

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