The Borrowing Processes of Thai Language in Patani Malay Dialect in Thailand

This study concerns with the processes borrowing Thai language (TL) words in Patani Malay Dialect (PMD) from the perspective of Sociolinguistics. The main objective of this study is to see how the processes happen from TL to PMD. The research data for TL loanwords in PMD are obtained from written and spoken language while the data from TL itself are obtained from written materials. Attention towards the processes of TL loanwords in PMD is in regard with the study about similarities between TL and Malay language (ML) through two important processes; importation and substitution. Indirectly this study also touched on the influence of TL which has an important role to the lexical elements of PMD. In addition to the elaboration of the borrowing process, an analysis was also carried out on the process of changing TL loanwords in PMD in terms of phonology involving vocal and consonant changes using descriptive approaches. Finally, the findings showed that apart from direct borrowing from TL, PMD also adapts the loanwords according to the existing system in PMD. Even from the borrowing elements themselves, it is found that there are words considered to come from the same family while some are borrowed from other languages.

Discussion on the Teaching Development of Thai Courses in Universities

In recent years, with the expansion and extension of China’s free trade platform, construction has gradually developed toward Southeast Asia and South Asia, thus resulting in the phenomena whereby Thai language courses have become more common and more universities have begun to cultivate Thai language majors. In this process, improving the effectiveness of Thai courses has become the key; therefore, schools and Thai language teachers should pay attention to this. In order to achieve the language teaching goal put forward by the Ministry of Education, it is necessary to accelerate the development of Thai courses in universities and explore a new development path for them, so that the teaching quality of Thai courses can be guaranteed. Based on this, this paper mainly discusses the development history of the curriculum of Thai language courses in universities.

Krathu-500: Post-Comments Thai Corpus

The Krathu-500 contains 574 Pantip posts title, post body with all comments of each post. The number of total comments is at 63,293 comments. The corpus provide Thai language used in real life situation with various context and types in conversational form. The corpus serves as a good way to improve capability of machine learning techniques that dealing with Thai language. Sentiment labeled smaller version of the comments dataset also provided with 6,306 records. The labeled corpus is human-annotated dataset with three labels for negative, neutral, and positive comments. The project also consists of open-source repository that allow any people who interested to modify and built on top of the current source code and dataset.

Krathu-500: Post-Comments Thai Corpus

The Krathu-500 contains 574 Pantip posts title, post body with all comments of each post. The number of total comments is at 63,293 comments. The corpus provide Thai language used in real life situation with various context and types in conversational form. The corpus serves as a good way to improve capability of machine learning techniques that dealing with Thai language. Sentiment labeled smaller version of the comments dataset also provided with 6,306 records. The labeled corpus is human-annotated dataset with three labels for negative, neutral, and positive comments. The project also consists of open-source repository that allow any people who interested to modify and built on top of the current source code and dataset.

Question Answering Model in Thai by using Squad Thai Wikipedia dataset

Nowadays, Question Answering is one of the challenge applications in the Natural language processing domain. There are plenty of English language Question Answering model distributed on the model sharing website such as Hugging face hub. Unlike Thai language, there is on a few Thai language Question Answering model distributed on the model sharing website. So, we decided to fine-tune a multilingual Question Answering model to a specify language which is Thai language. The datasets that we will use for training is a Thai Wikipedia dataset from iApp Technology. We have tried to fine-tune on two multilingual model. We also create another dataset to evaluate adaptivity of the model. The result came out to be as satisfy. Both fine-tuned models perform better than base model on evaluation score. We have published Question Answering model to Hugging face hub that will allow people to using these models for others application later.

Question Answering Model in Thai by using Squad Thai Wikipedia dataset

Nowadays, Question Answering is one of the challenge applications in the Natural language processing domain. There are plenty of English language Question Answering model distributed on the model sharing website such as Hugging face hub. Unlike Thai language, there is on a few Thai language Question Answering model distributed on the model sharing website. So, we decided to fine-tune a multilingual Question Answering model to a specify language which is Thai language. The datasets that we will use for training is a Thai Wikipedia dataset from iApp Technology. We have tried to fine-tune on two multilingual model. We also create another dataset to evaluate adaptivity of the model. The result came out to be as satisfy. Both fine-tuned models perform better than base model on evaluation score. We have published Question Answering model to Hugging face hub that will allow people to using these models for others application later.

Analysis of Gender Differences in Digital Game-Based Language Learning for Thai Language Learning Affection, Cognition and Progress Performance

This study aimed to use a Shaking Fun App with learning assessment and ranking learning as a teaching tool to allow Thai learning beginners to have digital game-style language learning, and to explore the gender differences in the perception of the cognitive and affective factors of the participants and the performance of gameplay progress based on the cognitive-affective theory of learning with media and embodying learning theory. In this study, a total of 246 Thai language learning beginners taking basic Thai (I) courses in 2 universities and 1 university of science and technology in northern Taiwan were invited to participate in the study. After those who dropped out were deducted and invalid data was deleted, there were 202 effective study participants including 82 males (40.6%) and 120 females (59.4%), and the effective recovery rate was 82.1%. After the reliability and validity analyses with SPSS 23.0, and the item analysis with AMOS 20.0, the gender differences were analyzed. The results showed that: there were indeed significant differences in participants of different genders in terms of gameplay flow, test anxiety and gameplay progress performance, but there was no significant difference in the continuance gameplay intention. In addition, using the Shaking Fun App for multiple weeks of DGBLL can indeed help learners to improve their game performance (Thai grammar).

Consonant Changes in Words Borrowed From Sanskrit to Thai and Patani Malay

Southeast Asia was under Indian influence for more than a thousand years so that the traces of Indian civilization can be determined from a lot of evidence. The entry of Indian civilization in this region has shown that Sanskrit has merged with Thai, the national language of Thailand, and Patani Malay, the mother tongue language of Thai Malays who live in the deep south of Thailand. Borrowing is a process of language contact and language change that can happen in all languages and is not limited to borrow in the same language family or the same type of language. All of them belong to different family trees. Sanskrit is a member of the Indo-European language family, whereas the Thai language is accepted to Tai-Kadai and Patani Malay belongs to the Austronesian language family. This study aims to study consonant changes of shared Sanskrit loanwords in Thai and Patani Malay. This research employed qualitative methodology. Data were collected from documentaries. The findings showed that changes in consonant phonemes occurred in both languages according to phonological adaptations such as deletion, insertion, voicing, devoicing, and substitution.