Raheem Sarwar


2024

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Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels
Kanishka Silva | Ingo Frommholz | Burcu Can | Fred Blain | Raheem Sarwar | Laura Ugolini
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

The advancement of generative Large Language Models (LLMs), capable of producing human-like texts, introduces challenges related to the authenticity of the text documents. This requires exploring potential forgery scenarios within the context of authorship attribution, especially in the literary domain. Particularly,two aspects of doubted authorship may arise in novels, as a novel may be imposed by a renowned author or include a copied writing style of a well-known novel. To address these concerns, we introduce Forged-GAN-BERT, a modified GANBERT-based model to improve the classification of forged novels in two data-augmentation aspects: via the Forged Novels Generator (i.e., ChatGPT) and the generator in GAN. Compared to other transformer-based models, the proposed Forged-GAN-BERT model demonstrates an improved performance with F1 scores of 0.97 and 0.71 for identifying forged novels in single-author and multi-author classification settings. Additionally, we explore different prompt categories for generating the forged novels to analyse the quality of the generated texts using different similarity distance measures, including ROUGE-1, Jaccard Similarity, Overlap Confident, and Cosine Similarity.

2023

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Authorship Attribution of Late 19th Century Novels using GAN-BERT
Kanishka Silva | Burcu Can | Frédéric Blain | Raheem Sarwar | Laura Ugolini | Ruslan Mitkov
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)

Authorship attribution aims to identify the author of an anonymous text. The task becomes even more worthwhile when it comes to literary works. For example, pen names were commonly used by female authors in the 19th century resulting in some literary works being incorrectly attributed or claimed. With this motivation, we collated a dataset of late 19th century novels in English. Due to the imbalance in the dataset and the unavailability of enough data per author, we employed the GANBERT model along with data sampling strategies to fine-tune a transformer-based model for authorship attribution. Differently from the earlier studies on the GAN-BERT model, we conducted transfer learning on comparatively smaller author subsets to train more focused author-specific models yielding performance over 0.88 accuracy and F1 scores. Furthermore, we observed that increasing the sample size has a negative impact on the model’s performance. Our research mainly contributes to the ongoing authorship attribution research using GAN-BERT architecture, especially in attributing disputed novelists in the late 19th century.

2021

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Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation
Peerat Limkonchotiwat | Wannaphong Phatthiyaphaibun | Raheem Sarwar | Ekapol Chuangsuwanich | Sarana Nutanong
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval
Nattapol Trijakwanich | Peerat Limkonchotiwat | Raheem Sarwar | Wannaphong Phatthiyaphaibun | Ekapol Chuangsuwanich | Sarana Nutanong
Findings of the Association for Computational Linguistics: EMNLP 2021

Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation. We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of-Domain (OOD) CLSR problems. In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments. In this way, we change the retrieval granularity from the sentence to the fragment level. We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE. Experimental results show that RFR significantly improves the base encoders’ performance for more than 85% of the cases.

2020

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Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble
Peerat Limkonchotiwat | Wannaphong Phatthiyaphaibun | Raheem Sarwar | Ekapol Chuangsuwanich | Sarana Nutanong
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent. Researchers have been relying on transfer learning to adapt an existing model to a new domain. However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as “black boxes”. We propose a filter-and-refine solution based on the stacked-ensemble learning paradigm to address this black-box limitation. We conducted extensive experimental studies comparing our method against state-of-the-art models and transfer learning. Experimental results show that our proposed solution is an effective domain adaptation method and has a similar performance as the transfer learning method.