Papers by Anar Yeginbergen
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques (2024.acl-long)
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| Challenge: | Recent work on sequence labelling has explored different strategies to mitigate the lack of manually annotated data for the large majority of the world languages. |
| Approach: | They propose to use the mask objective to exploit the few-shot capabilities of pre-trained language models to improve their performance. |
| Outcome: | The proposed model-transfer outperforms data-transference and fine-tuning outperformed few-shot methods for Argument Mining task. |
Dynamic Knowledge Integration for Evidence-Driven Counter-Argument Generation with Large Language Models (2025.findings-acl)
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| Challenge: | Argumentation in natural language processing (NLP) is becoming an indispensable tool in many application domains such as public policy, law, medicine, and education. |
| Approach: | They propose a reconstructed dataset of argument and counter-argument pairs . they propose integrating dynamic external knowledge from the web to improve counter-arguments . |
| Outcome: | The proposed method shows stronger correlation with human judgments compared to reference-based metrics. |
CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures (2024.emnlp-main)
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Ekaterina Sviridova, Anar Yeginbergen, Ainara Estarrona, Elena Cabrio, Serena Villata, Rodrigo Agerri
| Challenge: | Existing tools to aid residents in teaching medical doctors to explain decisions are a key objective of AI in education. |
| Approach: | They present a multilingual dataset for Medical Question Answering where doctors can annotate correct and incorrect diagnoses with argument components and argument relations. |
| Outcome: | The proposed dataset consists of 558 clinical cases with explanations in English, Spanish, French, Italian and annotated with argument components and argument relations. |