Disambiguated Lexically Constrained Neural Machine Translation (2023.findings-acl)
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| Challenge: | Current approaches to LCNMT assume that pre-specified lexicon constraints are contextually appropriate. |
| Approach: | They propose a framework that disambiguates constraints based on contexts at first and integrates them into LCNMT. |
| Outcome: | The proposed approach outperforms baseline approaches on benchmark datasets and comprehensive experiments in multiple target constraints. |
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| Challenge: | Existing work has not evaluated LNMT models under challenging real-world conditions. |
| Approach: | They propose a homograph disambiguation module and a model that integrates contextually rich information about unseen lexical constraints from pre-trained language models. |
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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. |
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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. |
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DictDis: Dictionary Constrained Disambiguation for Improved NMT (2024.findings-emnlp)
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| Challenge: | Existing approaches to domain-specific neural machine translation (NMT) are lexically constrained and draw from domain- specific dictionaries. |
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Integrating Vectorized Lexical Constraints for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on integrating discrete lexical constraints into neural machine translation models. |
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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. |
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Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation (N18-1)
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| Challenge: | Existing approaches to neural machine translation have computational complexities that are either linear or exponential in the number of constraints. |
| Approach: | They propose an algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. |
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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. |
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Training Neural Machine Translation to Apply Terminology Constraints (P19-1)
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| Challenge: | Existing methods to integrate domain terminology into neural machine translation (NMT) are brittle when tested in real-world situations. |
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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 . |
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