# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
## 2 Materials and Methods
### Translation and Multilingual Adaptation  
To enable multilingual capabilities in our NER pipeline, we employed machine translation models to convert the annotated English medical texts into several target languages, including German, Greek, Spanish, Italian, Polish, Portuguese, and Slovenian. These translations allowed us to develop a dataset that supports multilingual symptom extraction, enhancing the model's utility across diverse linguistic contexts. For the translations from English to German, Greek, Spanish, Italian, Polish, and Portuguese, we utilized models developed by the Language Technology Research Group at the University of Helsinki [24]. These models, licensed under CC-BY-4.0, are optimized for direct translations from English to each target language and are available on the Hugging Face platform [17]. Each model was selected specifically for its accuracy and reliability in medical contexts, given the specific terminology present in clinical texts:  
* German--Helsinki-NLP/opus-mt-en-de ([[https://huggingface.co/Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de)]([https://huggingface.co/Helsinki-NLP/opus-mt-en-de](https://huggingface.co/Helsinki-NLP/opus-mt-en-de)), accessed on 14 April 2025)
* Greek--Helsinki-NLP/opus-mt-en-el ([[https://huggingface.co/Helsinki-NLP/opus-mt-en-el](https://huggingface.co/Helsinki-NLP/opus-mt-en-el)]([https://huggingface.co/Helsinki-NLP/opus-mt-en-el](https://huggingface.co/Helsinki-NLP/opus-mt-en-el)), accessed on 14 April 2025)
* Spanish--Helsinki-NLP/opus-mt-tc-big-en-en-es ([[https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-es)]([https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-es](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-es)), accessed on 14 April 2025)
* Italian--Helsinki-NLP/opus-mt-tc-big-en-it ([[https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-it)]([https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-it](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-it)), accessed on 14 April 2025)
* Polish--gsarti/opus-mt-tc-en-pl ([[https://huggingface.co/gsarti/opus-mt-tc-en-pl](https://huggingface.co/gsarti/opus-mt-tc-en-pl)]([https://huggingface.co/gsarti/opus-mt-tc-en-pl](https://huggingface.co/gsarti/opus-mt-tc-en-pl)), accessed on 14 April 2025)
* Portuguese--Helsinki-NLP/opus-mt-tc-big-en-pt ([[https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt)]([https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-pt)), accessed on 14 April 2025)
* Slovenian--facebook/mbart-large-50-many-to-many-mmt model ([[https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt)]([https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt)), accessed on 14 April 2025) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Dataset** & **Entity Types** & **Number of Tokens (Train/Dev/Test)** & **F1 Score** \\ \hline i2b2 clinical model & i2b2-2010 corpus & Problem, Test, Treatment & 106,597/44,672/269,954 & 88.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stanza model’s description [18]. For English to Slovenian translations, we used the mBART model [25], a fine-tuned version of the mBART-large-50 model capable of translating between any pair of 50 languages. We chose this model due to its superior performance in handling low-resource languages like Slovenian. This model enables direct translation by setting the target language ID as the first generated token in the output sequence. This flexibility and multilingual adaptability were crucial in creating a reliable Slovenian translation.