Papers with iterative back-translation
Active Learning Approaches to Enhancing Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | a limited human translation budget is required to train neural machine translation models. |
| Approach: | They propose to integrate active learning into neural machine translation techniques . they propose a word frequency based acquisition function and an uncertainty based method . |
| Outcome: | The proposed method outperforms other acquisition functions on a limited human translation budget. |
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)
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| Challenge: | Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance. |
| Approach: | They propose a retrieval and rewriting based method to better initialize unsupervised translation models. |
| Outcome: | The proposed method improves translation performance by over 4 BLEU scores. |
Neural Machine Translation Models with Back-Translation for the Extremely Low-Resource Indigenous Language Bribri (2020.coling-main)
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| Challenge: | a small dataset of 5923 Bribri-Spanish pairs is used to train low-resource NMT models . |
| Approach: | They propose a Chibchan NMT model and dataset with an average performance of BLEU 16.91.7 for Bribri. |
| Outcome: | The proposed model improves on the Bribri dataset by 1.0 BLEU, but only when the new Spanish sentences belong to the same domain as the other Spanish examples. |
Decoupled Vocabulary Learning Enables Zero-Shot Translation from Unseen Languages (2024.acl-long)
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| Challenge: | Multilingual neural machine translation systems learn to map sentences of different languages into a common representation space. |
| Approach: | They propose a setup where we decouple learning of vocabulary and syntax and train to translate while keeping those word representations frozen. |
| Outcome: | The proposed setup achieves near parity with a supervised setting on the TED domain with varying number of languages seen by the encoder. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |