Learn and Consolidate: Continual Adaptation for Zero-Shot and Multilingual Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Existing multilingual neural machine translation models perform poorly on language pairs with no parallel corpus. |
| Approach: | They propose a two-stage approach that encourages original models to acquire language-agnostic multilingual representations from new data and preserves the model architecture without introducing parameters. |
| Outcome: | The proposed approach improves performance in translation directions where existing models are weak and mitigates degeneration in the well-performing translation directions, offering flexibility in the real-world scenario. |
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| Challenge: | Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions. |
| Approach: | They propose to build multilingual machine translation systems that serve arbitrary X-Y directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning. |
| Outcome: | The proposed system outperforms baseline bilingual models and pivot translation models in most directions without the need for architecture change or extra data collection. |
Continual Learning for Multilingual Neural Machine Translation via Dual Importance-based Model Division (2023.emnlp-main)
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| Challenge: | Existing methods focus on preventing catastrophic forgetting by making compromises between the original and new language pairs, leading to sub-optimal performance on both translation tasks. |
| Approach: | They propose a dual importance-based model division method to divide the model parameters into two parts and separate the translation of the original and new tasks. |
| Outcome: | The proposed method outperforms strong baselines under different incremental translation scenarios. |
Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation (2020.acl-main)
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| Challenge: | Existing approaches to improve multilingual neural machine translation (NMT) are weak, and lack robustness to support language pairs with varying typological characteristics. |
| Approach: | They propose to deepen NMT models to support language pairs with varying typological characteristics by random online backtranslation. |
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Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning. |
| Approach: | They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs. |
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Monolingual Adapters for Zero-Shot Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing adapter layers are more parameter-efficient and provide better performance than bilingual ones. |
| Approach: | They propose to use monolingual adapter layers instead of bilingual ones to compose them and generalize to unseen language pairs. |
| Outcome: | The proposed adapter layer formalism achieves a median improvement of +2.77 BLEU points over a 20-language multilingual Transformer baseline trained on TED talks. |
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation (2022.coling-1)
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| Challenge: | Existing studies attributed zero-shot translation to domination of central language, e.g. English, but we supplement this viewpoint with the strict dependence of non-centered languages. |
| Approach: | They propose a language-specific modeling method that adapts to non-centered languages to counteract the instability of zero-shot translation. |
| Outcome: | The proposed method performs better than baselines in centered data conditions and can easily fit non-centered data. |
From Bilingual to Multilingual Neural Machine Translation by Incremental Training (P19-2)
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| Challenge: | Existing approaches to multilingual neural machine translation are based on task specific models and the addition of one more language is only possible by retraining the whole system. |
| Approach: | They propose a training schedule that scales to more languages without modification of previous components. |
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An Empirical Investigation of Word Alignment Supervision for Zero-Shot Multilingual Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Recent work has highlighted several flaws of MNMT models in zero-shot scenarios where language labels are ignored and the wrong language is generated. |
| Approach: | They propose to combine explicit alignment to language labels with word alignment supervision to improve zero-shot translations. |
| Outcome: | The proposed model improves on three multilingual MT benchmarks. |
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. |
| Approach: | They will cover the latest advances in NMT approaches that leverage multilingualism . they will focus on topics such as language divergence, transfer learning and pivoting . |
| Outcome: | This tutorial will cover the latest advances in NMT to enhance low-resource translation models. |
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help? (2021.findings-emnlp)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) trains a single model that supports translation between multiple languages . transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. |
| Approach: | They propose a hierarchical knowledge distillation approach to train multilingual models . they use typological features and phylogeny to overcome negative transfer issue . |
| Outcome: | The proposed approach avoids negative transfer effect by capitalising on language groups generated according to typological features and phylogeny of languages. |