Papers with adapter-based approaches
BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer (2022.naacl-main)
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| Challenge: | Massively multilingual Transformers (MMTs) have dominated research in multilingual NLP and cross-lingual transfer recently. |
| Approach: | They propose to learn bilingual language pair adapters (BAs) when the goal is to optimize performance for a particular source-target transfer direction. |
| Outcome: | The proposed framework improves performance in three standard downstream tasks and for the majority of low-resource languages. |
Latent Group Dropout for Multilingual and Multidomain Machine Translation (2022.findings-naacl)
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| Challenge: | Multidomain and multilingual machine translation often rely on parameter sharing strategies, which are hardcoded in the network architecture, independent of the similarities between tasks. |
| Approach: | They propose a method to take advantage of similarities by using a latent-variable model and develop techniques to train this model end-to-end. |
| Outcome: | The proposed model improves translation performance without increasing the model size. |
xGQA: Cross-Lingual Visual Question Answering (2022.findings-acl)
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Jonas Pfeiffer, Gregor Geigle, Aishwarya Kamath, Jan-Martin Steitz, Stefan Roth, Ivan Vulić, Iryna Gurevych
| Challenge: | a lack of multilingual multimodal datasets has hindered multimodal vision and language modeling efforts. |
| Approach: | They propose a multilingual evaluation benchmark for the visual question answering task . they extend the established English GQA dataset to 7 typologically diverse languages . |
| Outcome: | The proposed methods outperform current state-of-the-art models in zero-shot cross-lingual settings, but the accuracy remains low across languages. |