Terminology-Aware Segmentation and Domain Feature for the WMT19 Biomedical Translation Task
In this work, we give a description of the TALP-UPC systems submitted for the WMT19 Biomedical Translation Task. Our proposed strategy is NMT model-independent and relies only on one ingredient, a biomedical terminology list. We first extracted such a terminology list by labelling biomedical words in our training dataset using the BabelNet API. Then, we designed a data preparation strategy to insert the terms information at a token level. Finally, we trained the Transformer model with this terms-informed data. Our best-submitted system ranked 2nd and 3rd for Spanish-English and English-Spanish translation directions, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
NMTTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
High Frequent In-domain Words Segmentation and Forward Translation for the WMT21 Biomedical Task
This paper reports the optimization of using the out-of-domain data in the Biomedical translation task. We firstly optimized our parallel training dataset using the BabelNet in-domain terminology words. Afterward, to inc…
TranslationElhuyar submission to the Biomedical Translation Task 2020 on terminology and abstracts translation
This article describes the systems submitted by Elhuyar to the 2020 Biomedical Translation Shared Task, specifically the systems presented in the subtasks of terminology translation for English-Basque and abstract transl…
TranslationHow Important Is Tokenization in French Medical Masked Language Models?
Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began wi…
Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model
Automatic recognition of biomedical entities in text is the crucial initial step in biomedical text mining. In this pa-per, we investigate employing modern neural network models for recognizing biomedical entities. To co…
Feature EngineeringLearning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition
End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in…
Feature Engineeringnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+2