Learning Language-Specific Layers for Multilingual Machine Translation (2023.acl-long)
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| Challenge: | Multilingual Machine Translation (MNMT) is a promising new approach to improve translation quality between non-English languages. |
| Approach: | They propose a language-specific transformer layer to increase model capacity while keeping computation and parameters constant. |
| Outcome: | The proposed approach improves translation quality by 1.3 chrF (1.5 spBLEU) over not using LSLs on a separate decoder architecture. |
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| Challenge: | State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. |
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Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)
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| Challenge: | Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model. |
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On the Multilingual Ability of Decoder-based Pre-trained Language Models: Finding and Controlling Language-Specific Neurons (2024.naacl-long)
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| Challenge: | Existing decoder-based pre-trained language models demonstrate excellent multilingual capabilities, but it is unclear how they handle multilingualism. |
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Multilingual Machine Translation with Hyper-Adapters (2022.emnlp-main)
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| Challenge: | Multilingual machine translation suffers from negative interference across languages. |
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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 . |
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Language-aware Interlingua for Multilingual Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing multilingual neural machine translation models fail to capture diversity and specificity of different languages, resulting in inferior performance against individual models that are sufficiently trained. |
| Approach: | They propose to integrate a language-aware interlingua into an Encoder-Decoder architecture to learn a semantic representation from the semantic spaces of different languages while allowing for language-specific specialization of a particular language pair. |
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Multilingual Neural Machine Translation (2020.coling-tutorials)
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| Challenge: | In this tutorial, we will cover the latest advances in NMT to enhance low-resource translation. |
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Importance-based Neuron Allocation for Multilingual Neural Machine Translation (2021.acl-long)
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Disentangling Pretrained Representation to Leverage Low-Resource Languages in Multilingual Machine Translation (2024.lrec-main)
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| Challenge: | Multilingual neural machine translation requires an enormous dataset, leaving the low-resource language (LRL) underdeveloped. |
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Learning Language Specific Sub-network for Multilingual Machine Translation (2021.acl-long)
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| Challenge: | Multilingual neural machine translation models suffer from performance degradation when learning multiple languages. |
| Approach: | They propose to use LaSS to jointly train a single unified multilingual MT model. |
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