Facilitating Terminology Translation with Target Lemma Annotations (2021.eacl-main)
Copied to clipboard
| Challenge: | Recent work on terminology integration assumes that the correct morphological forms are apriori known. |
| Approach: | They propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. |
| Outcome: | The proposed method improves terminology translation accuracy in Latvian and Baltic languages. |
Similar Papers
Towards the Machine Translation of Scientific Neologisms (2025.coling-main)
Copied to clipboard
| Challenge: | Scientific research continually discovers and invents new concepts, which are then referred to by new terms, neologisms, or nenonyms. |
| Approach: | They propose to leverage term definitions to translate neologisms with Large Language Models . they find that LLMs generate terms from co-hyponyms and terms sharing the same derivation paradigm . |
| Outcome: | The proposed model can generate terms from co-hyponyms and terms sharing the same derivation paradigm. |
Integrating Domain Terminology into Neural Machine Translation (2020.coling-main)
Copied to clipboard
| 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. |
Encouraging Neural Machine Translation to Satisfy Terminology Constraints (2021.findings-acl)
Copied to clipboard
| 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. |
Lemma Dilemma: On Lemma Generation Without Domain- or Language-Specific Training Data (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) can generate lemmas in context without prior fine-tuning. |
| Approach: | They compare in-context lemma generation with traditional fully supervised approaches . they use encoder-only supervised methods and cross-lingual methods . |
| Outcome: | The proposed model outperforms the traditional fully supervised approach in the context of lemmatization tasks. |
Target-Side Augmentation for Document-Level Machine Translation (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data. |
| Approach: | They propose a document-level machine translation model that generates many potential translations for each source document and smoothes the distribution. |
| Outcome: | The proposed method outperforms the previous best system by 2.30 s-BLEU on News and achieves new state-of-the-art on News . |
Rule-based Morphological Inflection Improves Neural Terminology Translation (2021.emnlp-main)
Copied to clipboard
| 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. |
Training Neural Machine Translation to Apply Terminology Constraints (P19-1)
Copied to clipboard
| 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. |
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Using a shared vocabulary is common practice in multilingual machine translation . however, when words overlap is small, e.g., using different writing systems, knowledge transfer is inhibited . |
| Approach: | They propose a re-parameterized method for building word embeddings using word equivalence classes and graph networks to fuse word embeds across languages. |
| Outcome: | The proposed method achieves evident BLEU improvements on high- and low-resource MNMT scenarios. |
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting (2023.emnlp-main)
Copied to clipboard
| Challenge: | idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. |
| Approach: | They propose to use retrieval-augmented models to increase the accuracy of a strong pretrained machine translation model on idiomatic sentences by up to 13%. |
| Outcome: | The proposed techniques improve the accuracy of a strong pretrained model on idiomatic sentences by up to 13% in absolute accuracy, and holds potential benefits for non-idiomatic phrases. |
Boosting Neural Machine Translation with Similar Translations (2020.acl-main)
Copied to clipboard
| Challenge: | Statistical Machine Translation and fuzzy matching are completely different in their finality. |
| Approach: | They propose to use fuzzy matching to train neural machine translation to make use of similar translations, in a similar way a human translator employs fuzzy matches. |
| Outcome: | The proposed methods improve translation accuracy and fine-tuned model for unseen translation pairs. |