Romina Etezadi


2024

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RFBES at SemEval-2024 Task 8: Investigating Syntactic and Semantic Features for Distinguishing AI-Generated and Human-Written Texts
Mohammad Heydari Rad | Farhan Farsi | Shayan Bali | Romina Etezadi | Mehrnoush Shamsfard
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

Nowadays, the usage of Large Language Models (LLMs) has increased, and LLMs have been used to generate texts in different languages and for different tasks. Additionally, due to the participation of remarkable companies such as Google and OpenAI, LLMs are now more accessible, and people can easily use them. However, an important issue is how we can detect AI-generated texts from human-written ones. In this article, we have investigated the problem of AI-generated text detection from two different aspects: semantics and syntax. Finally, we presented an AI model that can distinguish AI-generated texts from human-written ones with high accuracy on both multilingual and monolingual tasks using the M4 dataset. According to our results, using a semantic approach would be more helpful for detection. However, there is a lot of room for improvement in the syntactic approach, and it would be a good approach for future work.

2022

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DadmaTools: Natural Language Processing Toolkit for Persian Language
Romina Etezadi | Mohammad Karrabi | Najmeh Zare | Mohamad Bagher Sajadi | Mohammad Taher Pilehvar
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

We introduce DadmaTools, an open-source Python Natural Language Processing toolkit for the Persian language. The toolkit is a neural pipeline based on spaCy for several text processing tasks, including normalization, tokenization, lemmatization, part-of-speech, dependency parsing, constituency parsing, chunking, and ezafe detecting. DadmaTools relies on fine-tuning of ParsBERT using the PerDT dataset for most of the tasks. Dataset module and embedding module are included in DadmaTools that support different Persian datasets, embeddings, and commonly used functions for them. Our evaluations show that DadmaTools can attain state-of-the-art performance on multiple NLP tasks. The source code is freely available at https://github.com/Dadmatech/DadmaTools.

2021

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Improving Persian Relation Extraction Models By Data Augmentation
Moein Salimi Sartakhti | Romina Etezadi | Mehrnoush Shamsfard
Proceedings of the Second International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2021) co-located with ICNLSP 2021