Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Tekir, SelmaDepartment: search.filters.department.İzmir Institute of Technology

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  • Article
    Recognition of Counterfactual Statements in Turkish
    (Assoc Computing Machinery, 2025) Acar, Ali; Tekir, Selma
    Counterfactual statements are examples of causal reasoning as they describe events that did not happen and, optionally, those events' consequences if they happened. SemEval-2020 introduces the counterfactual detection (CFD) task and shares an English dataset. Since then, a set of datasets has been released in English, German, and Japanese as part of Amazon product reviews. This work releases the first Turkish corpus of counterfactuals (TRCD). The data collection process is driven by a clue phrase list of counterfactuals, mainly in the form of verb inflections in Turkish. We use clue phrase-based filtering to collect sentences from the Turkish National Corpus (TNC). On the other hand, half of the collection is subject to random word filtering to avoid selection bias due to clue phrases. After the human annotation process with an Inter Annotator Agreement of 0.65, we have 5000 sentences, of which 12.8% contain counterfactual statements. Furthermore, we provide a comprehensive baseline of transformer-based models by testing the effect of clue phrases, cross-lingual performance comparisons using the available CFD datasets, and zero-shot cross-lingual classification experiments using fine-tuning on the different combinations of the existing datasets. The results confirm that TRCD is compatible with the other CFD datasets. Moreover, fine-tuning a Turkish-specific model (BERTurk) performs better than the multilingual alternatives (mBERT and XLM-R). BERTurk is more robust to clue phrase masking. This result emphasizes the importance of a language-specific tokenizer for contextual understanding, especially for low-resource languages. Finally, our qualitative analysis gives insights into errors by different models.
  • Conference Object
    Citation - WoS: 4
    Citation - Scopus: 8
    LGPsolver - Solving Logic Grid Puzzles Automatically
    (Assoc Computational Linguistics-acl, 2020) Jabrayilzade, Elgun; Tekir, Selma
    Logic grid puzzle (LGP) is a type of word problem where the task is to solve a problem in logic. Constraints for the problem are given in the form of textual clues. Once these clues are transformed into formal logic, a deductive reasoning process provides the solution. Solving logic grid puzzles in a fully automatic manner has been a challenge since a precise understanding of clues is necessary to develop the corresponding formal logic representation. To meet this challenge, we propose a solution that uses a DistilBERT-based classifier to classify a clue into one of the predefined predicate types for logic grid puzzles. Another novelty of the proposed solution is the recognition of comparison structures in clues. By collecting comparative adjectives from existing dictionaries and utilizing a semantic framework to catch comparative quantifiers, the semantics of clues concerning comparison structures are better understood, ensuring conversion to correct logic representation. Our approach solves logic grid puzzles in a fully automated manner with 100% accuracy on the given puzzle datasets and outperforms state-of-the-art solutions by a large margin.
  • Conference Object
    Improvements on a Multi-Task Bert Model
    (Ieee, 2024) Agrali, Mahmut; Tekir, Selma
    Pre-trained language models have introduced significant performance boosts in natural language processing. Fine-tuning of these models using downstream tasks' supervised data further improves the acquired results. In the fine-tuning process, combining the learning of tasks is an effective approach. This paper proposes a multi-task learning framework based on BERT. To accomplish the tasks of sentiment analysis, paraphrase detection, and semantic text similarity, we include linear layers, a Siamese network with cosine similarity, and convolutional layers to the appropriate places in the architecture. We conducted an ablation study using Stanford Sentiment Treebank (SST), Quora, and SemEval STS datasets for each task to test the framework and its components' effectiveness. The results demonstrate that the proposed multi-task framework improves the performance of BERT. The best results obtained for sentiment analysis, paraphrase detection, and semantic text similarity are accuracies of 0.534 and 0.697 and a Pearson correlation coefficient of 0.345.