Papers by Tunga Gungor
Evaluating the Quality of a Corpus Annotation Scheme Using Pretrained Language Models (2024.lrec-main)
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Furkan Akkurt, Onur Gungor, Büşra Marşan, Tunga Gungor, Balkiz Ozturk Basaran, Arzucan Özgür, Susan Uskudarli
| Challenge: | Pretrained language models and large language models are increasingly used to assist in a variety of natural language processing tasks. |
| Approach: | They propose to use pretrained language models and large language models to evaluate their quality in natural language processing. |
| Outcome: | The proposed annotation scheme (2.11) yields sentences with higher success rate than the previous one. |
Data and Representation for Turkish Natural Language Inference (2020.emnlp-main)
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| Challenge: | Large annotated datasets in NLP are overwhelmingly in English . obtaining new annotation resources for each task in each language would be prohibitively expensive . |
| Approach: | They propose to use machine translation to translate large annotated datasets into Turkish . they find that in-language embeddings are essential and morphological parsing can be avoided . |
| Outcome: | The proposed model trains on human-translated evaluation sets. |
TULAP - An Accessible and Sustainable Platform for Turkish Natural Language Processing Resources (2023.eacl-demo)
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Susan Uskudarli, Muhammet Şen, Furkan Akkurt, Merve Gürbüz, Onur Gungor, Arzucan Özgür, Tunga Güngör
| Challenge: | a growing interest in the field of natural language processing is resulting in applications solving NLP tasks. |
| Approach: | They propose to create an open-source platform to share Turkish NLP resources . they propose to use the platform to publish open-sourced Turkish Nlp resources based on a research lab's datasets and tools. |
| Outcome: | The proposed platform is easy-to-use and publishes open-source Turkish NLP resources. |
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks (2022.findings-naacl)
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| Challenge: | Code-switching dependency parsing is a challenging task due to the scarcity of necessary resources and structural difficulties embedded in code-switch languages. |
| Approach: | They propose to use sequence labeling models as auxiliary tasks for code-switched dependency parsing in a semi-supervised scheme and acquire state-of-the-art scores on all studied languages. |
| Outcome: | The proposed model outperforms the previous model by 7.4 LAS points on average on all of the studied languages. |
TR-MTEB: A Comprehensive Benchmark and Embedding Model Suite for Turkish Sentence Representations (2025.findings-emnlp)
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| Challenge: | TR-MTEB is the first large-scale, task-diverse benchmark for sentence embedding models for Turkish. |
| Approach: | a new benchmark evaluates sentence embedding models for Turkish . TR-MTEB covers six core tasks and 26 high-quality datasets . |
| Outcome: | The TR-MTEB benchmark covers six core tasks and includes 26 high-quality datasets . the models achieve competitive performance across most tasks and significantly improve on baseline models. |