| Challenge: | Neural machine translation (NMT) performance drops when domains do not match and in-domain training data is scarce. |
| Approach: | They propose a curriculum learning approach to adapt generic neural machine translation models to a specific domain. |
| Outcome: | The proposed approach outperforms unadapted and adapted baselines in two domains and two language pairs. |
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. |
Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (N19-1)
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| Challenge: | a heterogeneous training dataset can vary in characteristics such as domain, translation quality, and degree of difficulty. |
| Approach: | They propose to use reinforcement learning to learn an optimal curriculum for NMT training . they find it can beat uniform baselines and hand-designed, state-of-the-art curricula . |
| Outcome: | The proposed approach beats baselines and hand-designed curricula on English-to-French datasets. |
Domain Differential Adaptation for Neural Machine Translation (D19-56)
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| Challenge: | Neural networks are data hungry and domain sensitive, so it is difficult to obtain labeled data for every domain. |
| Approach: | They propose a framework for domain adaptation where we model the difference between domains instead of smoothing over them. |
| Outcome: | The proposed framework improves on domain adaptation in multiple experimental settings. |
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. |
Integrating Domain Terminology into Neural Machine Translation (2020.coling-main)
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| Challenge: | Existing work on terminology integration into Neural Machine Translation shows it can dynamically specialize translation to a specific domain. |
| Approach: | They extend existing work on terminology integration into Neural Machine Translation . they use placeholders complemented by morphosyntactic annotation to integrate terminology . |
| Outcome: | The proposed method surpasses the surface generalization shown by other techniques. |
Token-wise Curriculum Learning for Neural Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing curriculum learning approaches to Neural Machine Translation (NMT) require sampling sufficient amounts of “easy” samples from training data at the early stage of training. |
| Approach: | They propose a token-wise curriculum learning approach that creates sufficient amounts of easy samples from training data. |
| Outcome: | The proposed approach outperforms baselines on five language pairs on low-resource languages. |
Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings (D19-1)
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| Challenge: | Recent studies have focused on domain adaptation for neural machine translation systems where in-domain data is scarce or nonexistent. |
| Approach: | They propose an approach that adapts models with domain-aware feature embeddings, which are learned via an auxiliary language modeling task. |
| Outcome: | The proposed model performs better in multiple experimental settings and with back translation. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |
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. |
Neural Unsupervised Domain Adaptation in NLP—A Survey (2020.coling-main)
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| Challenge: | Deep neural networks excel at learning from labeled data, but learning from unlabeled data remains a challenge. |
| Approach: | They review neural unsupervised domain adaptation techniques which do not require labeled target domain data. |
| Outcome: | The proposed techniques are more challenging yet widely applicable. |