Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | incorporating backtranslated data from different sources has led to improved results in machine translation (MT) |
| Approach: | They use a low-resource use-case and a high-resourced language pair to test different backtranslation scenarios and employ data selection to optimise the synthetic corpora. |
| Outcome: | The proposed method reduces the amount of data used while maintaining high-quality MT systems. |
Similar Papers
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
| Approach: | They propose a method that adds synthetic data to sentences with high prediction loss during training and a variety of sampling strategies targeting difficult-to-predict words. |
| Outcome: | The proposed method improves translation quality by up to 1.7 and 1.2 Bleu points over back-translation using random sampling for German-English and English-German, respectively. |
Understanding Back-Translation at Scale (D18-1)
Copied to clipboard
| Challenge: | An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. |
| Approach: | They propose to augment parallel training corpus with back-translations of target language sentences to improve neural machine translation with monolingual data. |
| Outcome: | The proposed method achieves a state-of-the-art of 35 BLEU on the WMT’14 English-German test set. |
Machine Translation for Low-Resource Languages through Monolingual Data and LLM: A Case Study of English-to-Basque (2026.eacl-srw)
Copied to clipboard
| Challenge: | Existing LLMs do not translate well from English to Basque, but they yield an acceptable performance in the reverse direction. |
| Approach: | They propose to use a Basque monolingual corpora to train an LLM-based MT system . they use 'sovereignty fine tuning' to generate parallel corporata, and then use preference optimization . |
| Outcome: | The proposed system improves translation quality in English-to-Basque direction while requiring limited data for low-resource languages. |
Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation (2023.acl-long)
Copied to clipboard
Jean Maillard, Cynthia Gao, Elahe Kalbassi, Kaushik Ram Sadagopan, Vedanuj Goswami, Philipp Koehn, Angela Fan, Francisco Guzman
| Challenge: | Existing datasets are not economical to create large-scale datasets, but for low-resource languages, a few thousand professionally translated sentence pairs can be useful. |
| Approach: | They propose to use a dataset to train machine translation models on pre-existing and synthetic data to augment them with millions of sentences through backtranslation. |
| Outcome: | The proposed model can cover hundreds of languages with high quality training data even when smaller but lower quality datasets are used. |
Language Model Priors and Data Augmentation Strategies for Low-resource Machine Translation: A Case Study Using Finnish to Northern Sámi (2024.findings-acl)
Copied to clipboard
| Challenge: | a new study examines the use of monolingual data for improving low-resource machine translation. |
| Approach: | They investigate ways of using monolingual data for improving low-resource machine translation. |
| Outcome: | The proposed model can perform better on the target-side data without augmentation of parallel data. |
Not Enough Data to Pre-train Your Language Model? MT to the Rescue! (2023.findings-acl)
Copied to clipboard
| Challenge: | In recent years, transformer-based language models (LMs) have become the default approach for many NLP tasks. |
| Approach: | They compare the performance of transformer-based language models with machine-translated corpora. |
| Outcome: | The proposed model can be improved with real data, but further research is needed. |
Many-to-English Machine Translation Tools, Data, and Pretrained Models (2021.acl-demo)
Copied to clipboard
| Challenge: | Commercial translation systems support only one hundred languages or fewer . commercial translation systems do not make these models available for transfer to low resource languages . |
| Approach: | They propose a multilingual neural machine translation model that can translate from 500 source languages to English. |
| Outcome: | The proposed model can translate from 500 source languages to English, or be used as a parent model for low-resource languages. |
Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
Copied to clipboard
| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
| Approach: | They propose to add a tag to back-translations to help distinguish back-translated data from original parallel training data. |
| Outcome: | The proposed tag helps the system distinguish back-translated data from original parallel training data and is as effective as a tag in high-resource training. |
Revisiting Machine Translation for Cross-lingual Classification (2023.emnlp-main)
Copied to clipboard
| Challenge: | Recent work in cross-lingual learning has pivoted around multilingual models, which are typically pretrained on unlabeled corpora in multiple languages using some form of language modeling objective. |
| Approach: | They propose to use a stronger machine translation system to mitigat mismatch between training on original text and running inference on machine translated text. |
| Outcome: | The proposed approach is highly task dependent and calls into question the dominance of multilingual models for cross-lingual classification. |
An Extensive Exploration of Back-Translation in 60 Languages (2023.findings-acl)
Copied to clipboard
| Challenge: | Back-translation has been shown to improve model quality through the creation of synthetic training bitext. |
| Approach: | They use back-translation to train models from 60 languages into English . early studies showed promise of the technique and follow on studies have produced refinements . |
| Outcome: | a new study shows that back-translation improves translation quality in low-resource languages . the results are consistent with previous studies, though there are limitations . |