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    <abstract>In this work, cross-linguistic span prediction based on contextualized word embedding models is used together with neural machine translation (NMT) to transfer and apply the state-of-the-art models in natural language processing (NLP) to a low-resource language clinical corpus. Two directions are evaluated: (a) English models can be applied to translated texts to subsequently transfer the predicted annotations to the source language and (b) existing high-quality annotations can be transferred beyond translation and then used to train NLP models in the target language. Effectiveness and loss of transmission is evaluated using the German Berlin-Tübingen-Oncology Corpus (BRONCO) dataset with transferred external data from NCBI disease, SemEval-2013 drug-drug interaction (DDI) and i2b2/VA 2010 data. The use of English models for translated clinical texts has always involved attempts to take full advantage of the benefits associated with them (large pre-trained biomedical word embeddings). To improve advances in this area, we provide a general-purpose pipeline to transfer any annotated BRAT or CoNLL format to various target languages. For the entity class medication, good results were obtained with 0.806 F1-score after re-alignment. Limited success occurred in the diagnosis and treatment class with results just below 0.5 F1-score due to differences in annotation guidelines.</abstract>
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%0 Conference Proceedings
%T Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language
%A Schäfer, Henning
%A Idrissi-Yaghir, Ahmad
%A Horn, Peter
%A Friedrich, Christoph
%Y Naumann, Tristan
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 4th Clinical Natural Language Processing Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, WA
%F schafer-etal-2022-cross
%X In this work, cross-linguistic span prediction based on contextualized word embedding models is used together with neural machine translation (NMT) to transfer and apply the state-of-the-art models in natural language processing (NLP) to a low-resource language clinical corpus. Two directions are evaluated: (a) English models can be applied to translated texts to subsequently transfer the predicted annotations to the source language and (b) existing high-quality annotations can be transferred beyond translation and then used to train NLP models in the target language. Effectiveness and loss of transmission is evaluated using the German Berlin-Tübingen-Oncology Corpus (BRONCO) dataset with transferred external data from NCBI disease, SemEval-2013 drug-drug interaction (DDI) and i2b2/VA 2010 data. The use of English models for translated clinical texts has always involved attempts to take full advantage of the benefits associated with them (large pre-trained biomedical word embeddings). To improve advances in this area, we provide a general-purpose pipeline to transfer any annotated BRAT or CoNLL format to various target languages. For the entity class medication, good results were obtained with 0.806 F1-score after re-alignment. Limited success occurred in the diagnosis and treatment class with results just below 0.5 F1-score due to differences in annotation guidelines.
%R 10.18653/v1/2022.clinicalnlp-1.6
%U https://aclanthology.org/2022.clinicalnlp-1.6
%U https://doi.org/10.18653/v1/2022.clinicalnlp-1.6
%P 53-62

Markdown (Informal)

[Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language](https://aclanthology.org/2022.clinicalnlp-1.6) (Schäfer et al., ClinicalNLP 2022)

• Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language (Schäfer et al., ClinicalNLP 2022)

ACL

• Henning Schäfer, Ahmad Idrissi-Yaghir, Peter Horn, and Christoph Friedrich. 2022. Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language. In Proceedings of the 4th Clinical Natural Language Processing Workshop, pages 53–62, Seattle, WA. Association for Computational Linguistics.