Papers with MultiUN
Triangular Architecture for Rare Language Translation (P18-1)
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| Challenge: | Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language. |
| Approach: | They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs. |
| Outcome: | Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods. |
Do Explicit Alignments Robustly Improve Multilingual Encoders? (2020.emnlp-main)
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| Challenge: | Explicit alignment objectives based on bitexts like Europarl and MultiUN have been shown to improve cross-lingual representations. |
| Approach: | They propose a new contrastive alignment objective that can better utilize bitexts . they propose to use a random sample of 1 million pair subset of OPUS data . |
| Outcome: | The proposed objective outperforms existing alignment objectives on a random 1 million pair subset of the OPUS dataset. |
LCS: A Language Converter Strategy for Zero-Shot Neural Machine Translation (2024.findings-acl)
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| Challenge: | Existing LT strategies cannot indicate the desired target language on zero-shot translation, i.e., the off-target issue. |
| Approach: | They propose a language converter strategy that embeds the target language into the top encoder layers to mitigate confusion in the encoder and ensures stable language indication for the decoder. |
| Outcome: | The proposed language converter strategy significantly mitigates off-target issue on multiUN, TED, and OPUS-100 datasets. |