Papers with language-independent representation

2 papers
Language-aware Interlingua for Multilingual Neural Machine Translation (2020.acl-main)

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Challenge: Existing multilingual neural machine translation models fail to capture diversity and specificity of different languages, resulting in inferior performance against individual models that are sufficiently trained.
Approach: They propose to integrate a language-aware interlingua into an Encoder-Decoder architecture to learn a semantic representation from the semantic spaces of different languages while allowing for language-specific specialization of a particular language pair.
Outcome: The proposed model achieves remarkable improvements over state-of-the-art multilingual NMT models and produces comparable performance with strong individual models.
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt (2022.emnlp-main)

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Challenge: Existing work focuses on monolingual prompts, but we study multilingual prompt for multilingual models.
Approach: They propose a model that uses a unified prompt for all languages, called UniPrompt, to alleviate the effort of designing different prompts for multiple languages.
Outcome: The proposed model outperforms baseline models in the zero-shot cross-lingual setting.

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