| Challenge: | Prior work has addressed the lack of gold standard code-mixed to pure language parallel data with data augmentation techniques. |
| Approach: | They propose a back-translation-based training scheme for code-mixed translation which eliminates dependence on external resources. |
| Outcome: | The proposed model beats previous work by up to +3.8 BLEU on code-mixed tasks. |
Similar Papers
Training Data Augmentation for Code-Mixed Translation (2021.naacl-main)
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| Challenge: | We show a 5.8 point increase in BLEU on heavily code-mixed sentences . code-mixing is becoming more commonplace in several bilingual communities . |
| Approach: | They propose a method to convert existing parallel data sources into code-mixed parallel data. |
| Outcome: | The proposed method shows a 5.8 point increase in BLEU on heavily code-mixed sentences on a Hindi-English code-mixed translation task. |
The Role of Mixed-Language Documents for Multilingual Large Language Model Pretraining (2026.acl-long)
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Jiandong Shao, Raphael Tang, Crystina Zhang, Karin Sevegnani, Pontus Stenetorp, Jianfei Yang, Yao Lu
| Challenge: | Existing research suggests that multilingual large language models can achieve impressive cross-lingual understanding despite largely monolingual pretraining. |
| Approach: | They compare a monolingual-only corpus with a standard web corpus that removes all multilingual documents and then retrain the models from scratch under controlled conditions. |
| Outcome: | The results show that removing bilingual data causes translation performance to drop 56% in BLEU, whereas code-switching contributes minimally. |
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 . |
An Analysis of Massively Multilingual Neural Machine Translation for Low-Resource Languages (2020.lrec-1)
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| Challenge: | In this study, we explore massively multilingual low-resource neural machine translation. |
| Approach: | They propose to use Bible translations to train models with up to 1,107 source languages and create multilingual corpora varying the number and relatedness of source languages. |
| Outcome: | The proposed approach is highly language-specific and can be tailored to the source language and its typology. |
T3L: Translate-and-Test Transfer Learning for Cross-Lingual Text Classification (2023.tacl-1)
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| Challenge: | Existing approaches to cross-lingual text classification leverage text classifiers trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Approach: | They propose to combine a neural machine translator and a text classifier trained in a high-resource language to perform text classification in other languages with no or minimal fine-tuning. |
| Outcome: | The proposed approach significantly improves over a baseline approach. |
A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models (2025.findings-naacl)
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| Challenge: | Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models. |
| Approach: | They investigate the impact of parallel corpora quality and quantity, training objectives, and model size on performance of multilingual large language models enhanced with parallel corporeal. |
| Outcome: | The proposed approach improves performance in bilingual and general-purpose tasks. |
Phylogeny-Inspired Adaptation of Multilingual Models to New Languages (2022.aacl-main)
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| Challenge: | Large pretrained multilingual models have delivered promising results due to cross-lingual learning capabilities on a variety of language tasks. |
| Approach: | They propose to use language phylogenetic information to improve cross-lingual transfer by leveraging closely related languages in a structured, linguistically-informed manner. |
| Outcome: | The proposed model significantly improves on the baseline model on languages unseen during training. |
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing multilingual machine translation approaches focus on English-centric directions, while non-English directions lag behind. |
| Approach: | They propose a multilingual machine translation system with an emphasis on non-English directions. |
| Outcome: | The proposed model outperforms existing models on English-centric and non-English directions on multilingual translation benchmarks. |
Rapid Adaptation of Neural Machine Translation to New Languages (D18-1)
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| Challenge: | Existing approaches to adapt neural machine translation systems to low-resource languages are difficult to implement and require large amounts of training data. |
| Approach: | They propose a method to train neural machine translation systems to new low-resource languages . they propose to start with massively multilingual "seed models" and continue training on data related to the LRL . |
| Outcome: | The proposed method achieves BLEU scores of up to 15.5 with no data from the LRL and improves over other adaptation methods by 1.7 BLUE points average over 4 LRL settings. |
Breaking Down Multilingual Machine Translation (2022.findings-acl)
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| Challenge: | Multilingual training is an essential ingredient in machine translation systems . but it has different effects in different multilingual settings, such as many-to-one, one-tomany and many- to-many learning . |
| Approach: | They compare multilingual training settings with encoders and decoders initialized by multilingual learning . they find important attention heads for each language pair and compare their correlations during inference . |
| Outcome: | The proposed models outperform the best models for high-resource languages and one-to-many models for low-resourced languages. |