| 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. |
Similar Papers
Learning a Multi-Domain Curriculum for Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing data selection methods do not work well for multiple domains . multiple aspects need to be considered for training a multi-domain model . |
| Approach: | They propose a dynamic data selection method to multi-domain NMT that incorporates instance-level domain-relevance features and a curriculum to gradually focus on multi- domain relevant data batches. |
| Outcome: | The proposed model outperforms no-curriculum training on multiple domains and reaches or outperformed individual performance. |
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation (2021.emnlp-main)
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| Challenge: | Building neural machine translation systems to perform well on a specific target domain remains a challenge. |
| Approach: | They propose to train a single NMT system per language pair that performs well across multiple domains. |
| Outcome: | The proposed approach improves the Pareto frontier on this task. |
Evaluating Domain Adaptation for Machine Translation Across Scenarios (L18-1)
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| Challenge: | Statistical machine translation (SMT) has been the dominant approach for the last 20 years, with neural machine translation becoming the new main paradigm in academic research and the industry. |
| Approach: | They propose to compare domain-adapted statistical and neural machine translation systems on three different domains and language pairs with varying degrees of domain specificity and available training data. |
| Outcome: | The proposed system is the best choice for translation, with marked impacts for domains with higher specificity. |
Domain Adaptive Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation models are effective when trained on broad domains with large datasets, such as news translation. |
| Approach: | They propose a novel approach for adaptive ensemble weighting for Neural Machine Translation by extending Bayesian Interpolation with source information. |
| Outcome: | The proposed approach improves performance on Spanish-English and English-German tasks without the need for the domain label. |
The Trade-offs of Domain Adaptation for Neural Language Models (2022.acl-long)
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| Challenge: | Neural Language Models (LMs) trained on large generic training sets have been shown to be effective at adapting to smaller, specific target domains for language modeling and other downstream tasks. |
| Approach: | They propose a framework for a Neural Language Models (LM) to be presented in a common framework. |
| Outcome: | The proposed framework highlights similarities and subtle differences between adaptation techniques and the framework. |
m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter (2022.findings-emnlp)
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| Challenge: | Multilingual neural machine translation models (MNMT) are effective on transferring knowledge between high-resource languages to low-resourced languages. |
| Approach: | They propose a multilingual multi-domain adapter which combines domain and language knowledge using meta-learning with adapters. |
| Outcome: | The proposed model outperforms other adapter methods in a domain shift and language pair translation task. |
Proceedings of the 3rd Workshop on Neural Generation and Translation (D19-56)
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| Challenge: | The third workshop on neural generation and translation is held in london . the workshop received 68 submissions from leading minds in the field . |
| Approach: | the third workshop on neural generation and translation is held in london . the workshop will feature four invited talks from leading minds in the field . |
| Outcome: | the third workshop on neural generation and translation is held in london . the conference received 68 submissions from which 36 accepted . |
On the Role of Parallel Data in Cross-lingual Transfer Learning (2023.findings-acl)
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| Challenge: | Existing multilingual models do not exploit the full potential of monolingual data, a new study finds . prior work has shown that parallel data is beneficial for cross-lingual learning, but it is unclear if it is the data itself or the modeling of parallel interactions that matters. |
| Approach: | They compare unsupervised machine translation to supervised machine translator and gold parallel data to generate synthetic parallel data. |
| Outcome: | The proposed model generated parallel data is better than supervised machine translation and gold parallel data in both general and task-specific settings. |
Distilling Multiple Domains for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Neural machine translation is a powerful tool for high-resource domains, but performance suffers when the input domain is low-resourced. |
| Approach: | They propose a framework for training a single multi-domain neural machine translation model that can translate multiple domains without increasing inference time or memory usage. |
| Outcome: | The proposed model improves translation on both high- and low-resource domains over strong multi-domain baselines and is robust under noisy data conditions. |
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. |