| Challenge: | Using common statistical measures for termhood and unithood, we identify terms from monolingual texts and investigate the contribution of terminology to translation quality. |
| Approach: | They propose to use common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings. |
| Outcome: | The proposed method has shown some reliability in automatically identifying terms in human translations, but drawbacks in handling low frequency terms and term variations shall be dealt with in the future. |
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
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Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms (C18-1)
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| Challenge: | Existing methods for extracting bilingual terminology from comparable corpora are limited to a set of syntactic patterns. |
| Approach: | They propose a framework for aligning bilingual terms independently of term lengths . they introduce some enhancements to the context-based and neural network based approaches . |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)
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| Challenge: | A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19. |
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| Outcome: | A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19. |
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches (2020.lrec-1)
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| Challenge: | Existing methods for multilingual terminology curation are limited as they do not fit the term within existing terminology. |
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Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks (2020.lrec-1)
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| Challenge: | Existing methods for assessing translation quality rely on manual features and external knowledge. |
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| Outcome: | The proposed model outperforms feature-based methods on a large human annotated dataset. |
An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers (2021.acl-short)
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| Challenge: | Existing word-level quality estimation models require labelled data for each language pair and expensive maintenance. |
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Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus (2020.acl-main)
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| Challenge: | Existing approaches to semantic role labeling (SRL) are focusing on the English language. |
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Learning to Translate Ambiguous Terminology by Preference Optimization on Post-Edits (2025.emnlp-industry)
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| Challenge: | Ambiguous terminology can make translation difficult, especially in corporate contexts. |
| Approach: | They propose to learn how to disambiguate terminology based on human post-edits . they use preference optimization to optimize for correctness using the term post-Edit . |
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Translating Domain-Specific Terminology in Typologically-Diverse Languages: A Study in Tax and Financial Education (2025.emnlp-main)
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| Challenge: | Existing public terminology datasets for MT research are limited in language coverage or domain specificity, making it difficult to assess or improve MT systems in specialized settings. |
| Approach: | They propose a multilingual terminology resource for tax and financial education covering seven typologically diverse languages: English, Spanish, Russian, Vietnamese, Korean, Chinese (traditional and simplified) and Haitian Creole. |
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