# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 1 Introduction  
Word segmentation is usually a preprocessing step for downstream natural language processing tasks, such as text classification, information retrieval, and machine translation. Like Chinese and Japanese, their scripts do not mark word boundaries with spaces, making word segmentation necessary. In addition, they are more pronounced in southeast Asian Abugida languages like Thai, Burmese, and Khmer. Moreover, their writing systems lack clear word boundaries. However, Thai, Burmese, and Khmer scripts differ from Chinese and Japanese in that a character represents just a consonant or a vowel, but a Chinese character represents a whole syllable. Hence, extending the Chinese word segmentation methods [1] to Thai, Burmese, and Khmer word segmentation problems is not straightforward. Complex orthographic structures present significant challenges for word segmentation tasks in southeast Asian Abugida scripts. These scripts encode consonant and vowel information within single syllables, frequently incorporating diacritics, stacked characters, and reordering rules. Such orthographic complexity introduces additional challenges to word segmentation, requiring models to effectively process both character and sub-word features. Another challenge is the ambiguity of word boundaries. In languages like Thai and Khmer, the absence of explicit markers between words often results in sequences of characters that can correspond to multiple words. For example, nonordiv (flower bush or hugging a wooden chest) can be segmented into nonordiv (flower bush) or nonordiv (hugging a wooden chest), but the latter is rather nonsensical and statistically unlikely. Thisambiguity necessitates a high reliance on contextual information, substantially increasing the computational demands on word segmentation models. To address the challenge of efficient and accurate word segmentation, several neural-based methods have been developed, such as Sertis [2], KhPOS [3], DeepCut [4], AttAttCut [5], THDICTSDR [6], and BERT+BiLSTMCRF+Fine-tune [7]. In these models, word segmentation is treated as a character-based sequence tagging task, employing various sequence encoders. Sertis and KhPOS models utilize bidirectional recurrent neural networks (RNNs) to capture contextual character information. In contrast, DeepCut and AttaCut replace RNNs with convolutional neural networks (CNNs) as sequence encoders. DeepCut leverages multiple convolutional filters with variable kernel widths to extract segmentation features from characters and character types but encounters slow segmentation speeds. Meanwhile, AttaCut leverages syllable embeddings as supplementary features and enhances processing speed through three dilated CNN filters. This performance boost comes at the expense of accuracy and necessitates a preprocessing step for syllable segmentation. THDICTSDR improves dictionary-based methods by applying Sparse Distributed Representations (SDRs) to learn contextual information and integrating n-grams to select the correct word, achieving accuracy close to or better than DeepCut in some cases. However, THDICTSDR still suffers from relatively high processing times, since it retrieves features from an external dictionary. The BERT+BiLSTMCRF+Fine-tune model leverages character, syllable, and word embeddings to capture rich syntactic and semantic features for word segmentation. However, because it relies on the BERT language model and the BiLSTM as its encoder, it is the largest model among these approaches, which significantly impacts its prediction speed, making it considerably slower. To develop an efficient and accurate word segmentation model for southeast Asian Abugida languages, it is essential to learn rich and effective segmentation features from sequences of characters and sub-words while also maintaining time efficiency in contextual modeling. The windowed context modeling strategy is more efficient than RNNs-based models, which is shown in DeepCut [4] and AttaCut [5] models.