Wannaphong Phatthiyaphaibun


2023

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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 https://github.com/pythainlp/pythainlp.

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.