Word segmentation is a challenging pre-processing step for Thai Natural Language Processing due to the lack of explicit word boundaries.The previous systems rely on powerful neural network architecture alone and ignore linguistic substructures of Thai words. We utilize the linguistic observation that Thai strings can be segmented into syllables, which should narrow down the search space for the word boundaries and provide helpful features. Here, we propose a neural Thai Word Segmenter that uses syllable embeddings to capture linguistic constraints and uses dilated CNN filters to capture the environment of each character. Within this goal, we develop the first ML-based Thai orthographical syllable segmenter, which yields syllable embeddings to be used as features by the word segmenter. Our word segmentation system outperforms the previous state-of-the-art system in both speed and accuracy on both in-domain and out-domain datasets.

We aim to provide computational evidence for the era of authorship of two important old Thai texts: Traiphumikatha and Pumratchatham. The era of authorship of these two books is still an ongoing debate among Thai literature scholars. Analysis of old Thai texts present a challenge for standard natural language processing techniques, due to the lack of corpora necessary for building old Thai word and syllable segmentation. We propose an accurate and interpretable model to classify each segment as one of the three eras of authorship (Sukhothai, Ayuddhya, or Rattanakosin) without sophisticated linguistic preprocessing. Contrary to previous hypotheses, our model suggests that both books were written during the Sukhothai era. Moreover, the second half of the Pumratchtham is uncharacteristic of the Sukhothai era, which may have confounded literary scholars in the past. Further, our model reveals that the most indicative linguistic changes stem from unidirectional grammaticalized words and polyfunctional words, which show up as most dominant features in the model.

Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Many neural network models have been proposed to tackle this problem. However, the comparison for this task is not unified, so we could hardly draw clear conclusions about the effectiveness of various architectures. Here, we propose neural network models that are based on feedforward and long-short term memory architecture and systematically study the effects of varying structures. To our surprise, the best-configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Further, we compare our best feedforward system with competitive convolutional and recurrent networks and find that feedforward can actually be more effective. For the first time for this task, we compile and publish outputs from previous neural and non-neural systems to establish the standard for further comparison.