Papers by Raheem Sarwar
Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels (2024.eacl-srw)
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| Challenge: | generative Large Language Models (LLMs) are capable of producing human-like texts, but they pose challenges related to the authenticity of the text documents. |
| Approach: | They propose a modified GANBERT-based model to improve the classification of forged novels via the Forged Novels Generator and the generator in GAN. |
| Outcome: | The proposed model improves classification of forged novels in two data-augmentation aspects. |
Authorship Attribution of Late 19th Century Novels using GAN-BERT (2023.acl-srw)
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| Challenge: | Conventional techniques and neural networks are the two main authorship attribution methods. |
| Approach: | They used a dataset of late 19th century novels in English to fine-tune a transformer-based authorship attribution model using transfer learning. |
| Outcome: | The proposed model outperforms the existing model with 0.88 accuracy and F1 scores. |
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval (2021.findings-emnlp)
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Nattapol Trijakwanich, Peerat Limkonchotiwat, Raheem Sarwar, Wannaphong Phatthiyaphaibun, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. |
| Approach: | They propose a framework for cross-lingual sentence retrieval that uses a collection of fragments to improve sentence retrievals. |
| Outcome: | The proposed framework improves the retrieval robustness of the base sentences encoded by m-USE, LASER, and LaBSE. |
Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation (2021.findings-acl)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu . a framework to handle out-of-domain inputs is proposed to improve word segmentation . |
| Approach: | They propose a domaingeneric domain adaptation framework and data augmentation technique to combat low-resource problems. |
| Outcome: | The proposed model outperforms the state-of-the-art Thai word segmentation method in out-of domain scenarios. |
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (2020.emnlp-main)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Thai word segmentation is domain-dependent, and researchers have been relying on transfer learning to adapt existing models to new domains. |
| Approach: | They propose a filter-and-refine solution to address Thai word segmentation as a domain-dependent problem. |
| Outcome: | The proposed method is an effective domain adaptation method and has similar performance as the transfer learning method. |