| Challenge: | Existing methods to integrate domain terminology into neural machine translation (NMT) are brittle when tested in real-world situations. |
| Approach: | They propose a method to inject custom terminology into neural machine translation at run time by using the target side of terminology entries whose source side match the input as decoding-time constraints. |
| Outcome: | The proposed method is faster than state-of-the-art decoding and more efficient than constraint-free decoding. |
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Encouraging Neural Machine Translation to Satisfy Terminology Constraints (2021.findings-acl)
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| Challenge: | a new approach to encourage neural machine translation to satisfy lexical constraints is proposed . a BLEU score and percentage of generated constraint terms are improved by the proposed method . |
| Approach: | They propose a method that encourages neural machine translation to satisfy lexical constraints at training step . they use a simplified augmentation strategy without source factors and constraint token masking to make it easier to learn the copy behavior . |
| Outcome: | The proposed method improves on baselines in terms of BLEU score and percentage of generated constraint terms. |
Neural Machine Translation Decoding with Terminology Constraints (N18-2)
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| Challenge: | Constrained neural machine translation systems can provide excellent quality but do not strictly enforce terminology. |
| Approach: | They propose a framework for constrained neural decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. |
| Outcome: | The proposed framework performs well on multiple translation tasks and motivates the need for constrained decoding with attentions to reduce misplacement and duplication when translating user constraints. |
Lexically Constrained Neural Machine Translation with Levenshtein Transformer (2020.acl-main)
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| Challenge: | Existing approaches to incorporate lexical constraints in neural machine translation have been unsuccessful . |
| Approach: | They propose an algorithm that incorporates lexical constraints into neural machine translation. |
| Outcome: | The proposed method improves on English-German datasets without modification . it does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. |
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. |
Restricted or Not: A General Training Framework for Neural Machine Translation (2022.acl-srw)
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| Challenge: | Existing work imposes constraints on beam search decoding, which limits the concurrent processing ability of the model in deployment. |
| Approach: | They propose a general training framework that allows a model to support both restricted and unrestricted translations by adopting an additional auxiliary training process without constraining the decoding process. |
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Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction (2021.acl-short)
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| Challenge: | Accurate terminology translation is crucial for ensuring the practicality and reliability of neural machine translation systems. |
| Approach: | They propose a method to preserve terminology in translations as lexical constraints with or without a term dictionary at test time. |
| Outcome: | The proposed setup achieves consistent improvements on terminology and sentence-level translation for three domain-specific corpora in two language pairs. |
A Template-based Method for Constrained Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to solve this problem can not satisfy the following three desiderata: (1) high translation quality, (2) high match accuracy, and (3) low latency. |
| Approach: | They propose a template-based method that can provide high translation quality and match accuracy and a low latency inference. |
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End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages (2021.acl-long)
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| Challenge: | Existing approaches to enforce word forms in translations struggle to make them agree with the rest of the output. |
| Approach: | They propose to train neural machine translation models with lemmatized constraints to infer correct word inflection. |
| Outcome: | The proposed model reduces errors in translation of constrained terms in automatic and manual evaluations on English-Czech language pairs. |
Rule-based Morphological Inflection Improves Neural Terminology Translation (2021.emnlp-main)
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| Challenge: | Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. |
| Approach: | They propose a framework for incorporating lemma constraints in machine translation . they use a cross-lingual inflection module that inflects the target lemmo constraints based on the source context. |
| Outcome: | The proposed framework outperforms existing methods with lower training costs and linguistic knowledge in domain adaptation and low-resource MT settings. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
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| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |