Kanungsukkasem, Nont

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Kanungsukkasem, Nont
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Kanunasukkasem, Nont
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nont.ka@kmitl.ac.th

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    Item type:Publication,
    Thai Generalized Alignment Task (TGAT): A Corpus and Comparative Study for Hallucination Detection in Thai
    (2025-01-01)
    Wangwon, Sivakorn
    ;
    Pitijaroonpong, Rapepong
    ;
    Chuangkrud, Piyawat
    ;
    Damrongrat, Chaianun
    ;
    Kongyoung, Sarawoot
    Large Language Models (LLMs) face critical reliability challenges due to hallucination-the generation of factually inaccurate content. While hallucination detection has advanced for major languages, Thai remains underserved, lacking specialized alignment models and datasets. We introduce the Thai Generalized Alignment Task (TGAT), a seven-subtask corpus tailored for Thai hallucination detection, spanning Fact Verification, Natural Language Inference, Information Retrieval, Question Answering, Summarization, Semantic Textual Similarity, and Paraphrase Identification. We conduct a comparative study across transformer families-encoder-only, encoder-decoder, and decoder-only-evaluated on the test set of each sub-task to analyze architectural compatibility and generalization for Thai hallucination detection. We also perform an ablation study to quantify how the presence of each subtask dataset affects the overall average performance, clarifying the role of dataset composition. Our experiments show that decoder-only models consistently outperform encoder-decoder and encoder-only alternatives under a standardized zero-shot prompting setup, establishing strong baselines and offering guidance for model selection and dataset design in low-resource settings.
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    Keyword-Text Graph Representation for Short Text Classification
    (2021-01-01)
    Chuanakrud, Piyawat
    ;
    Leelanupab, Teerapong
    ;
    Damrongrat, Chaianun
    ;
    Kanunasukkasem, Nont
    Short text classification is an essential task in Natural Language Processing. This task is widely applied to many applications, such as spam filtering, question-answering, artificial conversational agent, sentiment analysis, review mining, etc. Short texts usually encounter a great challenge for classification due to data sparseness as they do not provide sufficient contextual information. In this paper, we introduce Keyword-Text Graph Convolutional Networks (KwTGCN) for short text classification. We also propose a method to identify keywords by estimating word distribution over different categories. These category keywords are then used to build a special keyword-text graph of short text corpus. We employ Graph Convolutional Network (GCN) and our keyword-text graph to generate the representation of short text corpus based on the relations of document-keyword and document-word as well as the word co-occurrence. This document, word and keyword representation is further used as an input feature for the next layer of short text classification. The experimental results on multiple benchmark datasets show that our proposed model outperforms the state-of-the-art models for short text classification in multiple attempts.
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    Item type:Publication,
    When are Latent Topics Useful for Text Mining?: Enriching Bag-of-Words Representations with Information Extraction in Thai News Articles
    (2023-01-01)
    Kanungsukkasem, Nont
    ;
    Chuangkrud, Piyawat
    ;
    Pitichotchokphokhin, Pimpitcha
    ;
    Damrongrat, Chaianun
    ;
    Leelanupab, Teerapong
    The Bag-of-Words (BOW) model is simple but one of the successful representations of text documents. This model, however, suffers from the sparse matrix, in which most of the elements are zero. Topic modeling is an unsupervised learning method that can represent text documents in a low-dimensional space. Latent Dirichlet Allocation (LDA) is a topic modeling technique used for topic extraction and data exploration, with interpretable output. This paper presents a thorough study of potential benefits of applying LDA, as a feature extraction, to topic discovery and document classification in Thai news articles, comparing with TF–IDF and Word2Vec. We also studied how much of the top Thai terms extracted from LDA with the different numbers of topics can be interpretable and meaningful, and can be a representative of the corpus. Besides, a set of Topic Coherence measures were included in our study to estimate the degree of semantic similarity of extracted topics. To compare the performance and optimization time of classification of features from the different feature extraction methods, various types of classifiers, e.g., Logistic Regression, Random Forest, XGBoosting, etc., were experimented.