Papers with low-resource language pairs
Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair (2025.naacl-srw)
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| Challenge: | Existing methods to train machine translation models for low-resource languages are not available. |
| Approach: | They propose to build a machine translation model for code-switched Kazakh-Russian language pair with no labeled data. |
| Outcome: | The proposed method beats an existing commercial system by human evaluation on a Kazakh-Russian language pair with no labeled data. |
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora (N18-4)
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| Challenge: | Recent crowdsourcing efforts and workshops on machine translation have resulted in small amounts of parallel texts for building viable machine translation systems for low resource pairs. |
| Approach: | They propose to use an end-to-end Siamese bidirectional recurrent neural network to extract parallel sentences from Wikipedia to improve BLEU scores on both NMT and phrase-based SMT systems. |
| Outcome: | The proposed approach improves BLEU scores on both NMT and phrase-based SMT systems for the low-resource language pairs English–Hindi and English–Tamil when compared to training exclusively on the limited bilingual corpora. |
Parallel sentences mining with transfer learning in an unsupervised setting (2021.naacl-srw)
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| Challenge: | Existing methods to mine parallel sentences in low-resource environments are not suitable for many low-level language pairs. |
| Approach: | They propose an approach based on transfer learning to mine parallel sentences in an unsupervised setting using bilingual corpora of low-resource language pairs. |
| Outcome: | The proposed model improves the performance of mined parallel sentences at two real-world low-resource language pairs compared with previous methods. |
DaCoM: Strategies to Construct Domain-specific Low-resource Language Machine Translation Dataset (2025.coling-industry)
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| Challenge: | Existing models for low-resource languages struggle with domain-specific terms and lack of expert annotators for dataset creation. |
| Approach: | They propose a method for collecting low-resource language pairs from industrial domains using a large language model and neural machine translation framework. |
| Outcome: | The proposed model performs poorly on DaCoM-created datasets with up to 53.7 BLEURT points difference depending on domain inclusion. |
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. |
English-Basque Statistical and Neural Machine Translation (L18-1)
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| Challenge: | Neural machine translation (NMT) requires large training corpora, which is problematic for low-resource languages. |
| Approach: | They propose to use an open-domain and an IT-domain corpora to train machine translations in English-Basque. |
| Outcome: | The proposed systems outperform OpenNMT, Moses SMT and Google Translate in English-Basque translation. |
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts (L18-1)
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| Challenge: | Chinese and Portuguese are very populous languages, but there is not much parallel corpora in the Chinese-Portuguese language pair. |
| Approach: | They propose to curate Chinese-Portuguese parallel corpora and evaluate their quality . they extract bilingual data from government websites and use Phrased-Based Machine Translation (PBMT) and Neural Machine Translation models to build large corpus. |
| Outcome: | The proposed method can be used as a benchmark for future Chinese-Portuguese MT systems. |
AugVic: Exploiting BiText Vicinity for Low-Resource NMT (2021.findings-acl)
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| Challenge: | Neural Machine Translation (NMT) systems often exhibit poor performance due to the lack of large bitext training corpora in low-resource languages. |
| Approach: | They propose a data augmentation framework which exploits the vicinal samples of the given bitext without using extra monolingual data explicitly. |
| Outcome: | The proposed framework can diversify in-domain bitext data with finer level control on four low-resource language pairs. |
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations (P19-1)
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Rui Zhang, Caitlin Westerfield, Sungrok Shim, Garrett Bingham, Alexander Fabbri, William Hu, Neha Verma, Dragomir Radev
| Challenge: | Experimental results show that our model outperforms competitive translation-based baselines on cross-lingual relevance ranking tasks. |
| Approach: | They propose to match queries and documents in both source and target languages with deep bilingual query-document representations. |
| Outcome: | The proposed model outperforms translation-based baselines on English-Swahili, English-Tagalog, and English-Somali cross-lingual retrieval tasks. |
Visual Pivoting Unsupervised Multimodal Machine Translation in Low-Resource Distant Language Pairs (2024.findings-emnlp)
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| Challenge: | Existing studies show that neural MT achieves much worse translation quality than statistical MT with a small number of corpora. |
| Approach: | They propose a visual pivoting method for alignment between distant language pairs . they first construct a dataset and then apply it to pre-training and fine-tuning . |
| Outcome: | The proposed method outperforms baselines on DLPs and close language pairs. |
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation (2020.acl-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. |
| Approach: | They propose a method to translate between 13 languages using a single encoder and a decoder . they propose two knowledge distillation methods to further enhance multilingual UNMT performance . |
| Outcome: | The proposed method improves translation performance for all languages using multilingual data. |
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale (2024.naacl-long)
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| Challenge: | Multilingual machine translation (MMT) is a key tool for improving translation in low-resource languages. |
| Approach: | They examine how denoising autoencoding and backtranslation impact multilingual machine translation under different data conditions and model scales. |
| Outcome: | The proposed method improves translation efficiency in low-resource languages by using denoising autoencoding (DAE) and backtranslation (BT) . |
Unsupervised Extraction of Partial Translations for Neural Machine Translation (N19-1)
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| Challenge: | Neural machine translation systems usually require a large quantity of bilingual parallel data for training. |
| Approach: | They propose an algorithm for extracting from monolingual data what they call partial translations . partial translation is a pair of source and target sentences that contain sequences of tokens that are translations of each other. |
| Outcome: | The proposed algorithm extracts from monolingual data what we call partial translations . it takes only source and target monolingual datasets as input . |
Cross-lingual Feature Extraction from Monolingual Corpora for Low-resource Unsupervised Bilingual Lexicon Induction (2022.coling-1)
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| Challenge: | Unsupervised bilingual lexicon induction models fail on low-resource language pairs due to insufficient initialization. |
| Approach: | They propose a method to learn cross-lingual features from monolingual corpora for low-resource UBLI by integrating cross-linguistic representations with pre-trained word embeddings in a fully unsupervised initialization. |
| Outcome: | The proposed method outperforms state-of-the-art methods on low-resource language pairs and improves representational ability and robustness of existing embedding models. |
Data and Parameter Scaling Laws for Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent work shows that supervised neural machine translation models scale like a power law with the amount of training data and number of non-embedding parameters in the model. |
| Approach: | They show that cross-entropy loss of supervised neural machine translation models scales like a power law with the amount of training data and number of non-embedding parameters in the model. |
| Outcome: | The proposed model can predict BLEU and ROI of labeling data in low-resource language pairs. |
SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages (2022.emnlp-main)
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Alireza Mohammadshahi, Vassilina Nikoulina, Alexandre Berard, Caroline Brun, James Henderson, Laurent Besacier
| Challenge: | Existing models for multilingual machine translation use scaling up the number of parameters to overcome the curse of multilinguality. |
| Approach: | They propose a multilingual machine translation model that shares information between similar languages and scales up the number of parameters to overcome the curse of multilinguality. |
| Outcome: | The proposed model outperforms previous models on low-resource benchmarks while improving inference latency and memory usage. |
Language-agnostic Representation from Multilingual Sentence Encoders for Cross-lingual Similarity Estimation (2021.emnlp-main)
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| Challenge: | Existing methods to extract language-specific information from multilingual sentence embeddings are remarkably successful in cross-lingual and multilingual NLU tasks. |
| Approach: | They propose to extract language-specific information from the original embedding and use it to retrieve an embeddable that fully represents the sentence’s meaning. |
| Outcome: | The proposed method outperforms baselines on cross-lingual sentences even in low-resource language pairs where only tens of thousands of parallel sentence pairs are available. |