Papers with Back-translation
BLEU might be Guilty but References are not Innocent (2020.emnlp-main)
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| Challenge: | Using a method to collect references and compare their value with human evaluations, we show that multi-reference BLEU does not improve the correlation for high quality output. |
| Approach: | They propose a method to compare the quality of automated metrics by analyzing references and comparing them with human evaluations. |
| Outcome: | The proposed method improves correlation with all modern evaluation metrics including embedding-based methods. |
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation (2024.findings-eacl)
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| Challenge: | Current multilingual agreement (MA) methods require parallel data between multiple language pairs, which is not always realistic and optimize the agreement in an ambiguous direction, which hampers the translation performance. |
| Approach: | They propose a novel multilingual agreement framework that optimizes agreement bidirectionally with the Kullback-Leibler Divergence loss. |
| Outcome: | The proposed method improves strong baselines on the task of multilingual neural machine translation with three benchmarks: TED Talks, News, and Europarl. |
A Semantic Uncertainty Sampling Strategy for Back-Translation in Low-Resources Neural Machine Translation (2025.acl-srw)
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Yepai Jia, Yatu Ji, Xiang Xue, Shilei@imufe.edu.cn Shilei@imufe.edu.cn, Qing-Dao-Er-Ji Ren, Nier Wu, Na Liu, Chen Zhao, Fu Liu
| Challenge: | Back-translation methods rely on large-scale parallel corpora to enhance performance, but ignore the semantic quality of monolingual data. |
| Approach: | They propose a method which prioritizes sentences with higher semantic uncertainty as training samples by computationally evaluating the complexity of unannotated monolingual data. |
| Outcome: | The proposed method improves translation accuracy and fluency by +1.7 on all three translation tasks. |
Exploiting Curriculum Learning in Unsupervised Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Experimental results show that the proposed method achieves consistent improvements with faster convergence speed. |
| Approach: | They propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities. |
| Outcome: | The proposed method achieves consistent improvements with faster convergence speed on WMT 14 En-Fr, WMT14 En-De, and LDC En-Zh translation tasks. |
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages (2023.eacl-main)
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| Challenge: | Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been effective for a broad spectrum of downstream software engineering tasks. |
| Approach: | They propose to combine a source-to-target model with a target-tosource model trained in parallel. |
| Outcome: | The proposed approach performs competitively with state-of-the-art methods. |
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation (N19-1)
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| Challenge: | Back-translation has been used in previous approaches for unsupervised neural machine translation, but pseudo sentences are of low quality as translation errors accumulate during training. |
| Approach: | They propose an approach to extract and edit real sentences from monolingual corpora and introduce a comparative translation loss to evaluate the translated target sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art translation systems across two benchmarks and two low-resource language pairs by more than 2 BLEU points. |
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints (2021.emnlp-main)
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| Challenge: | HintedBT provides hints (as source tags on the encoder) about the quality of each source-target pair. |
| Approach: | They propose a method which provides hints to the encoder and decoder to improve the quality of BT data by providing hints about the quality. |
| Outcome: | The proposed method improves translation quality and performance in three low/medium-resource language pairs. |
On The Evaluation of Machine Translation Systems Trained With Back-Translation (2020.acl-main)
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| Challenge: | Back-translation is a data augmentation technique that can be used to improve neural machine translation systems. |
| Approach: | They propose to combine back-translation with a language model score to measure fluency. |
| Outcome: | The proposed method improves translation quality of natural text and translationese according to professional translators. |
Iterative Constrained Back-Translation for Unsupervised Domain Adaptation of Machine Translation (2022.coling-1)
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| Challenge: | Existing back-translation methods focus on in-domain lexical knowledge, which may lead to poor translation of unseen in- domain words. |
| Approach: | They propose an iterative constrained back-translation method to incorporate in-domain lexical knowledge into synthetic parallel data from BT. |
| Outcome: | The proposed method improves the BLEU score by up to 3.08 on four domains. |
Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation (2022.acl-long)
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| Challenge: | Experimental results show that backtranslation improves UNMT performance by reducing the data gap between training and inference. |
| Approach: | They propose an online method to remedy the source discrepancy between training and inference . they use pseudo parallel data with translated source and translated target to mimic inference scenario . |
| Outcome: | The proposed method outperforms baselines on several widely-used language pairs by remedying the style and content gaps. |
Dynamic Data Selection and Weighting for Iterative Back-Translation (2020.emnlp-main)
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| Challenge: | Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines. |
| Approach: | They propose a data selection and weighting strategy to iterate back-translation models and apply it to it . they use a target language to back-transcribe monolingual data, which is of high quality and reflect the target domain. |
| Outcome: | The proposed approach achieves 1.8 BLEU points over baselines on domain adaptation, low-resource, and high-resourced MT settings and on two language pairs. |
An Extensive Exploration of Back-Translation in 60 Languages (2023.findings-acl)
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| 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 . |
Visual Information Guided Zero-Shot Paraphrase Generation (2022.coling-1)
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| Challenge: | Several studies use different information as ”pivot” such as language, semantic representation and so on. |
| Approach: | They propose to use visual information as the "pivot" of back-translation to generate paraphrases using paired image-caption data. |
| Outcome: | The proposed model generates paraphrase with good relevancy, fluency and diversity . it is based on paired image-caption data and can train a paraphrasing model . |
Transplant Then Regenerate: A New Paradigm for Text Data Augmentation (2025.emnlp-main)
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| Challenge: | Data augmentation is a critical technique in deep learning. |
| Approach: | They propose a novel text augmentation paradigm leveraging large language models . they incorporate seed text into a context expanded by LLM and ask it to regenerate a variant based on the expanded context. |
| Outcome: | The proposed model generates high-quality and diverse augmented text with a transplant-then-regenerate approach. |