Papers with unsupervised translation
Unsupervised Pivot Translation for Distant Languages (P19-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a popular method for transferring information between languages. |
| Approach: | They propose an unsupervised pivot translation method which translates a language to a distant language through multiple hops. |
| Outcome: | The proposed method improves translation on 20 languages and 294 distant languages on 20 different languages and language pairs. |
GATITOS: Using a New Multilingual Lexicon for Low-resource Machine Translation (2023.emnlp-main)
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| Challenge: | a new study explores the effectiveness of bilingual lexica in machine translation models . cross-lingual vocabulary alignment is still highly imperfect in these models, despite the success of supervised and self-supervised training. |
| Approach: | They use a resource to improve translation performance on 200-language models . they show that lexica is more reliable than human-translated data . |
| Outcome: | The proposed approach improves on 200-language translation models with lexical data augmentation . the proposed approach is open-source and has 168 tail languages . |
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages (2021.naacl-main)
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| Challenge: | Unsupervised translation systems have impressive performance on resource-rich language pairs . however, in more realistic settings, unsupervised systems perform poorly . |
| Approach: | They propose a model for 5 low-resource languages that leverages monolingual and auxiliary parallel data from other high-resourced languages. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages . it also matches the current state- of-the art model for Nepali-English . |
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder (D18-1)
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| Challenge: | Unsupervised learning of cross-lingual word embeddings has fundamental limitations in translating sentences. |
| Approach: | They propose a method to improve word-by-word translation of cross-lingual embeddings using monolingual corpora without any back-translation. |
| Outcome: | The proposed system surpasses state-of-the-art unsupervised translation systems without costly iterative training. |
A Multilingual View of Unsupervised Machine Translation (2020.findings-emnlp)
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| Challenge: | Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
| Approach: | They propose a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. |
| Outcome: | The proposed framework achieves higher BLEU scores than state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
Unsupervised Paraphrasing without Translation (P19-1)
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| Challenge: | Recent work on automatic paraphrasing focuses on methods leveraging machine translation as an intermediate step. |
| Approach: | They propose to learn paraphrasing models only from a monolingual corpus . they propose a residual variant of vector-quantized variational auto-encoder . |
| Outcome: | The proposed model outperforms supervised and unsupervised translation methods in paraphrase identification and training set augmentation. |