| Challenge: | Existing methods to reduce interference in multilingual machine translation are often computationally intensive and do not always work. |
| Approach: | They propose to reduce interference in multilingual machine translation models by enlarging the model and tuning the sampling temperature to control the proportion of each language pair in the data. |
| Outcome: | The proposed model size, data size, and proportion of each language pair within the dataset determine interference (or synergy) . |
Similar Papers
Counter-Interference Adapter for Multilingual Machine Translation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to multilingual machine translation suffer from performance degradation, resulting in a single model being inferior to separately trained bilingual models on resource-rich languages. |
| Approach: | They propose a transformer-based model with a small parameter overhead for multilingual machine translation that outperforms strong multilingual baselines on 64 of 66 language directions. |
| Outcome: | The proposed model outperforms strong multilingual baselines on 64 of 66 language directions, 42 of which have above 0.5 BLEU improvement. |
On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment (2020.emnlp-main)
Copied to clipboard
| Challenge: | Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer) however, recent work has shown that this approach can degrade performance on high-resource languages, a phenomenon known as negative interference. |
| Approach: | They propose a meta-learning algorithm that adds language-specific parameters as meta-parameters and trains them in a manner that explicitly improves shared layers’ generalization on all languages. |
| Outcome: | The proposed model improves cross-lingual transferability and generalization on all languages, and improves on the language-specific parameters. |
Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
Copied to clipboard
| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
| Outcome: | The proposed methods can be applied to encoder models and encoder-decoder-only models . they show that language-neutral and language-specific information is key . |
Understanding the effects of language-specific class imbalance in multilingual fine-tuning (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing methods to fine-tune large language models have been developed to reduce the amount of resources needed to perform classification tasks. |
| Approach: | They modify traditional class weighing approach to reduce imbalance by calculating class weights separately for each language. |
| Outcome: | The proposed model improves performance and reduces the promotion of uninformative features. |
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | Transfer learning is a promising direction for low-resource neural machine translation (NMT) but it introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. |
| Approach: | They conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. |
| Outcome: | The results suggest that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural machine translation. |
A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models (2025.findings-naacl)
Copied to clipboard
| 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. |
Revisiting Multi-Domain Machine Translation (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data. |
| Approach: | They propose to use domain adaptation methods to handle situations where a sample of matched sentences is available in training and where only samples of source-side sentences are available. |
| Outcome: | The proposed model is able to handle multiple domains and their expectations with respect to performance. |
When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale (2024.naacl-long)
Copied to clipboard
| 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) . |
When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages (2024.emnlp-main)
Copied to clipboard
| Challenge: | Multilingual language models are widely used to extend NLP systems to low-resource languages. |
| Approach: | They pre-train over 10,000 monolingual and multilingual language models for over 250 languages including multiple language families that are under-studied in NLP. |
| Outcome: | The results show that adding multilingual data improves low-resource language modeling performance, similar to increasing low-source dataset sizes by up to 33%. |
Breaking Down Multilingual Machine Translation (2022.findings-acl)
Copied to clipboard
| 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. |