A Comparative Study of Pretrained Language Models on Thai Social Text Categorization

TL;DR

This study shows that pretraining large language models on noisy Thai social media data significantly improves performance on social text categorization tasks, especially in resource-scarce settings.

Contribution

It introduces a large Thai social media corpus and evaluates four modern language models, highlighting effective pretraining and fine-tuning strategies for Thai social text classification.

Findings

Pretraining on 1.26 billion tokens enhances classification accuracy.

BERT outperforms other models in downstream tasks.

Pretraining benefits are evident even with limited data.

Abstract

The ever-growing volume of data of user-generated content on social media provides a nearly unlimited corpus of unlabeled data even in languages where resources are scarce. In this paper, we demonstrate that state-of-the-art results on two Thai social text categorization tasks can be realized by pretraining a language model on a large noisy Thai social media corpus of over 1.26 billion tokens and later fine-tuned on the downstream classification tasks. Due to the linguistically noisy and domain-specific nature of the content, our unique data preprocessing steps designed for Thai social media were utilized to ease the training comprehension of the model. We compared four modern language models: ULMFiT, ELMo with biLSTM, OpenAI GPT, and BERT. We systematically compared the models across different dimensions including speed of pretraining and fine-tuning, perplexity, downstream classification benchmarks, and performance in limited pretraining data.