| Challenge: | Existing corpora of parallel corporata are being used in the biomedical domain . MT is known to support readers' access to textual documents in a language other than their native language . |
| Approach: | They propose to leverage parallel corpora to implement cross-lingual information retrieval or machine translation tools. |
| Outcome: | The proposed corpus is being used in the biomedical task at the conference on machine translation (WMT'16 and WMT'17) it can be leveraged to provide access to health information in languages other than English. |
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| Challenge: | Existing corpora are used to train and evaluate machine translation systems, but little information is available about the methods used for producing the corpus, including translation direction. |
| Approach: | They used PubMed and publisher websites to obtain contact information for MEDLINE authors and asked about their abstract writing practices. |
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A Multilingual Parallel Corpora Collection Effort for Indian Languages (2020.lrec-1)
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| Challenge: | Currently, neural network based approaches for machine translation are data hungry and sentence-level aligned parallel pairs are the currency. |
| Approach: | They propose to build sentence aligned parallel corpora across 10 Indian languages using online sources which have content shared across languages. |
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Leveraging High-Resource English Corpora for Cross-lingual Domain Adaptation in Low-Resource Japanese Medicine via Continued Pre-training (2025.findings-emnlp)
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Kazuma Kobayashi, Zhen Wan, Fei Cheng, Yuma Tsuta, Xin Zhao, Junfeng Jiang, Jiahao Huang, Zhiyi Huang, Yusuke Oda, Rio Yokota, Yuki Arase, Daisuke Kawahara, Akiko Aizawa, Sadao Kurohashi
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SciPar: A Collection of Parallel Corpora from Scientific Abstracts (2022.lrec-1)
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Dimitrios Roussis, Vassilis Papavassiliou, Prokopis Prokopidis, Stelios Piperidis, Vassilis Katsouros
| Challenge: | SciPar is a collection of parallel corpora created from openly available metadata of bachelor theses, master theses and doctoral dissertations hosted in institutional repositories, digital libraries and national archives. |
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BioMegatron: Larger Biomedical Domain Language Model (2020.emnlp-main)
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Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, Raghav Mani
| Challenge: | Existing studies on domain language models do not study the factors affecting performance on domain languages. |
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A Large Parallel Corpus of Full-Text Scientific Articles (L18-1)
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| Challenge: | Scielo database contains articles from several research domains. |
| Approach: | They propose to build a parallel corpus from Scielo in three languages: English, Portuguese, and Spanish. |
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A Recipe of Parallel Corpora Exploitation for Multilingual Large Language Models (2025.findings-naacl)
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| Challenge: | Recent studies have highlighted the potential of exploiting parallel corpora to enhance multilingual large language models. |
| Approach: | They investigate the impact of parallel corpora quality and quantity, training objectives, and model size on performance of multilingual large language models enhanced with parallel corporeal. |
| Outcome: | The proposed approach improves performance in bilingual and general-purpose tasks. |
MultiMSD: A Corpus for Multilingual Medical Text Simplification from Online Medical References (2025.findings-acl)
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| Challenge: | Medical texts contain technical terms, and non-experts often cannot use information effectively. |
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MedMT5: An Open-Source Multilingual Text-to-Text LLM for the Medical Domain (2024.lrec-main)
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Iker García-Ferrero, Rodrigo Agerri, Aitziber Atutxa Salazar, Elena Cabrio, Iker de la Iglesia, Alberto Lavelli, Bernardo Magnini, Benjamin Molinet, Johana Ramirez-Romero, German Rigau, Jose Maria Villa-Gonzalez, Serena Villata, Andrea Zaninello
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A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature (P18-1)
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| Challenge: | In 2015 alone, about 100 manuscripts describing randomized controlled trials for medical interventions were published every day. |
| Approach: | They propose a corpus of 5,000 medical articles annotated with demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured. |
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