Lalita Lowphansirikul


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PyThaiNLP: Thai Natural Language Processing in Python
Wannaphong Phatthiyaphaibun | Korakot Chaovavanich | Charin Polpanumas | Arthit Suriyawongkul | Lalita Lowphansirikul | Pattarawat Chormai | Peerat Limkonchotiwat | Thanathip Suntorntip | Can Udomcharoenchaikit
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)

We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThaiNLP. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThaiNLP in their work. The library is freely available at


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ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation
Peerat Limkonchotiwat | Wuttikorn Ponwitayarat | Lalita Lowphansirikul | Can Udomcharoenchaikit | Ekapol Chuangsuwanich | Sarana Nutanong
Findings of the Association for Computational Linguistics: EMNLP 2022

Sentence representations are essential in many NLP tasks operating at the sentence level. Recently, research attention has shifted towards learning how to represent sentences without any annotations, i.e., unsupervised representation learning. Despite the benefit of training without supervised data, there is still a performance penalty compared to supervised methods. Furthermore, the supervised-unsupervised performance gap widens as we reduce the model size. In this paper, we propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap, especially for smaller models. Utilizing the concept for knowledge distillation, we derive a distillation framework comprising two training objectives, control and generalize, called ConGen. Experiments on semantic textual similarity (STS), text classification (transfer), and natural language inference (NLI) tasks show that ConGen is on par with supervised training even on smaller models. Furthermore, our method consistently outperformed competitors on multilingual STS.The code and models are available at