Papers by Attapol Rutherford
Syllable-based Neural Thai Word Segmentation (2020.coling-main)
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| Challenge: | Word segmentation is a challenging pre-processing step for Thai Natural Language Processing due to the lack of explicit word boundaries. |
| Approach: | They propose a neural Thai word segmenter that uses syllable embeddings to capture linguistic constraints and dilated CNN filters to capture the environment of each character. |
| Outcome: | The proposed system outperforms the previous state-of-the-art system on both in-domain and out-domain datasets. |
Learning Job Title Representation from Job Description Aggregation Network (2024.findings-acl)
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Napat Laosaengpha, Thanit Tativannarat, Chawan Piansaddhayanon, Attapol Rutherford, Ekapol Chuangsuwanich
| Challenge: | Existing methods for learning job title representation neglect the rich content within the job description. |
| Approach: | They propose a framework for learning job titles through their respective job description and utilize a Job Description Aggregator component to handle the lengthy description and bidirectional contrastive loss. |
| Outcome: | The proposed framework outperforms the skill-based approach on in-domain and out-of-domain settings and achieving a superior performance. |
More Than Words: Collocation Retokenization for Latent Dirichlet Allocation Models (2022.findings-acl)
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| Challenge: | Latent Dirichlet Allocation models ingest words to discover their latent topics . but it is unclear how to achieve the best results for languages without marked word boundaries . |
| Approach: | They propose to use retokenization to merge frequent token ngrams into collocations in input to a Latent Dirichlet Allocation model. |
| Outcome: | The proposed model improves topic coherence and coherency in Chinese and Thai . the proposed model is more coherent and clearer than unmerged models . |
Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models (2025.coling-main)
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| Challenge: | Rapid advancements in large language models have highlighted the need for robust evaluation frameworks that assess their core capabilities. |
| Approach: | They propose two benchmarks to assess core capabilities of large language models . current benchmarks for Thai focus mainly on traditional NLP tasks . |
| Outcome: | The proposed benchmarks are based on evaluations of various LLMs with multi-lingual capabilities and are publicly available to encourage further research and development for Thai LLM. |
Thai Nested Named Entity Recognition Corpus (2022.findings-acl)
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Weerayut Buaphet, Can Udomcharoenchaikit, Peerat Limkonchotiwat, Attapol Rutherford, Sarana Nutanong
| Challenge: | a new dataset for Named Entity Recognition (NER) is proposed for Thailand. |
| Approach: | They propose to use Thai N-NER to extract named entities from text . they propose to include a nested structure that can be used to improve NER . |
| Outcome: | The proposed dataset is the largest non-English N-NER dataset and the first non- English one with fine-grained classes. |