# UnifiedCut: A Simple and Efficient Neural Model for Thai, Burmese and Khmer Word Segmentation
## 2 Related Works  
### Word Segmentation  
At present, the main research methods for word segmentation tasks in the Thai [5; 8; 9], Burmese [7; 10; 11] and Khmer [12; 13; 14] are divided into rule-based, traditional machine learning and neural network methods. Common traditional machine learning methods mainly include hidden Markov models (HMMs), maximum entropy models (MEMs), and conditional random fields (CRFs), and so on. In recent years, deep learning has made great breakthroughs in the field of natural language processing, such as RNNs, CNNs, BiLSTM and pre-training-based methods. The dictionary-based or rule-based word segmentation methods are efficient [15; 16; 17], but they depend too much on the quality of the dictionaries, and their ability to recognize new words is insufficient. [PERSON] et al. propose a statistics-based method [18], in which word n-gram and the Generalized Left-to-right Reduce (GLR) analysis algorithm are used. In this technique, an input Thai text is segmented into a sequence of Thai Character Clusters (TCCs) that is inseparable based on Thai writing rules, which help reduce the chances of segmenting words incorrectly. Although it can automatically exclude some ambiguities and identify out-of-vocabulary and yield better performance than the dictionary-based method, the model has a large parameter space resulting in the curse of dimension. [PERSON] et al. [19] proposed a combination of rule-based heuristic methods and statistical methods for Burmese word segmentation. This methods segment Burmese words by artificially summarizing the combination characteristics of the Burmese syllables, but the word segmentation effect is heavily dependent on the manually defined word segmentation rules. Due to the drawbacks of the dictionary-based and statistics-based methods, machine learning (ML) methods, especially neural network models, have been adopted for word segmentation. [PERSON] et al. [13] proposed a bidirectional maximum matching approach to maximize segmentation accuracy. The model processed maximum matching twice-forward and backward, however, it was unable to handle out-of-vocabulary words (OOV) and take into account the context. [PERSON] et al. [10] proposed a Burmese word segmentation method based on CRF and feature clustering. Moreover, CRF requires a customized language feature model so that the process is more complicated. Furthermore, the error rate can be high if the model definition is inaccurate. At present, the mainstream is to use neural network method, which can automatically learn the feature representation of related tasks, and the effect is obviously better than statistical machine learning. Two usually adopted neural networks structures are RNN and CNN. [PERSON] et al. [2] apply bidirectional RNN to Thai word segmentation, which is independent on artificial features. Compared with CNN, RNN needs more training time because of its recurrent nature. Subsequently, [PERSON] et al. [4] propose a CNN-based Thai word segmentation model named DeepCut, which uses characters and character category features as input features and has high performance. [PERSON] et al. [5] propose Thai word segmentation models named AttaCut-C and AttaCut-SC, which are also based on CNN. Both models employ the dilation technique in the convolutional layers [20; 21], which allows the models to use non-redundant convolution layers that cover sufficient context. Their architecture has less computational dependencies and hence a higher degree of parallelism than DeepCut. In addition, AttaCut-SC uses syllable embeddings as additional features, which provides higher-level information than using character information alone. But it utilizes syllable embeddings as the representation, which causes the model to require additional preprocessing steps. However, the model in this paper only usescharacter-level embedding as the representation to avoid the error propagation of syllable segmentation. [PERSON] et al. [3] proposed joint word segmentation and a POS tagging Khmer model using a deep learning approach.