Papers with multilingual foundation model

1 papers
BinaryAlign: Word Alignment as Binary Sequence Labeling (2024.acl-long)

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Challenge: State-of-the-art word alignment training methods require a different class depending on the availability of gold data for a particular language pair.
Approach: They propose a novel word alignment technique based on binary sequence labeling that outperforms existing approaches in both scenarios.
Outcome: The proposed method outperforms existing models on non-English language pairs and performs stratified error analysis over alignment error type.

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