Papers by Ekapol Chuangsuwanich
Machine translation Evaluation Eng-Thai MQM Ranking dataset (2026.eacl-short)
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Phichet Phuangrot, Natdanai Trintawat, Kanawat Vilasri, Yanapat Patcharawiwatpong, Pachara Boonsarngsuk, Nat Pavasant, Ekapol Chuangsuwanich
| Challenge: | MEET-MR provides a comprehensive benchmark for evaluating English–Thai machine translation systems. |
| Approach: | They propose a benchmark for evaluating English–Thai machine translation systems . they use the Multidimensional Quality Metrics framework to provide fine-grained human judgements of translation quality. |
| Outcome: | The dataset covers nine domains providing linguistic and contextual diversity. |
Towards Better Understanding of Program-of-Thought Reasoning in Cross-Lingual and Multilingual Environments (2025.findings-acl)
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Patomporn Payoungkhamdee, Pume Tuchinda, Jinheon Baek, Samuel Cahyawijaya, Can Udomcharoenchaikit, Potsawee Manakul, Peerat Limkonchotiwat, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Multi-step reasoning is essential for large language models, yet multilingual performance remains challenging. |
| Approach: | They propose a framework to evaluate Program-of-Thought (PoT) prompting by separating multilingual reasoning from code execution to examine impact of fine-tuning on question-reasoning alignment and reasoning quality. |
| Outcome: | The proposed framework outperforms CoT fine-tuned models in multilingual settings and shows strong correlation between reasoning quality and answer accuracy. |
SEA-BED: How Do Embedding Models Represent Southeast Asian Languages? (2026.acl-long)
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Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng, Jann Railey Montalan, Thura Aung, Jian Gang Ngui, Yosephine Susanto, William Chandra Tjhi, Panuthep Tasawong, Erik Cambria, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | SEA-BED examines how multilingual text embeddings perform across tasks and languages . performance gaps arise from data coverage, training objectives, and architectural design, authors say . |
| Approach: | They propose a large-scale benchmark covering 10 SEA languages and diverse embedding tasks. |
| Outcome: | The proposed model performs poorly across languages and tasks, but language-task analyses reveal inconsistencies . the results suggest that performance gaps arise from limitations in data coverage, training objectives, and architectural design. |
Mitigating Spurious Correlation in Natural Language Understanding with Counterfactual Inference (2022.emnlp-main)
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Can Udomcharoenchaikit, Wuttikorn Ponwitayarat, Patomporn Payoungkhamdee, Kanruethai Masuk, Weerayut Buaphet, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing approaches to debias NLU models rely on superficial patterns to produce correct predictions . lexical overlap and annotation artifacts can be used to make shortcuts . |
| Approach: | They propose a causal analysis framework to help debias NLU models by defining causal relationships and utilizing counterfactual inference to mitigate bias. |
| Outcome: | The proposed framework can improve robustness across three NLU tasks while maintaining high in-distribution performance. |
McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering (2024.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing approaches struggle with consistency across multiple languages and multi-size input scenarios. |
| Approach: | They propose a cross-lingual training framework that leverages multi-task learning to enhance cross-linguistic consistency and ranking stability. |
| Outcome: | The proposed training framework outperforms competitors on various input sizes and architectures. |
Space Decomposition for Sentence Embedding (2024.findings-acl)
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| Challenge: | Existing methods to measure sentence pair similarity are based on a continuous semantic textual similarity scale . however, the score in the range [4,5] indicates an upper-range sample, while the rest are lower-range samples. |
| Approach: | They propose a method to decompose sentences into embedding space space . they use a mixture of specialized projectors to distinguish and rank upper-range and lower-range samples . |
| Outcome: | The proposed method outperforms existing methods on STS and zero-shot benchmarks while reducing overlap between upper-range and lower-range classes. |
An Empirical Study of Multilingual Reasoning Distillation for Question Answering (2024.emnlp-main)
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Patomporn Payoungkhamdee, Peerat Limkonchotiwat, Jinheon Baek, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing efforts to distill reasoning capabilities have focused mainly on English, leaving multilingual distillation underexplored. |
| Approach: | They propose a method that incorporates incorrect rationales as additional guidance to improve multilingual reasoning in large language models. |
| Outcome: | Empirical results show that d-CoT-nR significantly surpasses the baseline, improving accuracy in unseen languages and correctness in step-by-step reasoning. |
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation (2022.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Sentence representations are essential in many NLP tasks operating at the sentence level. |
| Approach: | They propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap for smaller models. |
| Outcome: | The proposed method outperforms supervised training on STS, text classification, and natural language inference tasks on smaller models. |
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. |
Typo-Robust Representation Learning for Dense Retrieval (2023.acl-short)
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Panuthep Tasawong, Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Dense retrieval is a fundamental building block of information retrieval applications. |
| Approach: | They propose a method that aligns misspelled queries with their pristine counterparts to improve contrast between each query and its surrounding queries. |
| Outcome: | The proposed method outperforms the competitors in all cases with misspelled queries. |
Evaluating Perspectival Biases in Cross-Modal Retrieval (2026.findings-acl)
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Teerapol Saengsukhiran, Peerawat Chomphooyod, Narabodee Rodjananant, Chompakorn Chaksangchaichot, Patawee Prakrankamanant, Witthawin Sripheanpol, Pak Lovichit, Sarana Nutanong, Ekapol Chuangsuwanich
| Challenge: | a recent study shows that multimodal retrieval systems are expected to operate in a semantic space, agnostic to the language or cultural origin of the query. |
| Approach: | They introduce a benchmark to quantify linguistic and cultural biases in multimodal retrieval systems . they propose a framework to decouple language from culture and decouples it from semantics . |
| Outcome: | The proposed benchmark systematically measures the effects of linguistic and cultural biases on retrieval performance. |
Identifying and Mitigating Annotation Bias in Natural Language Understanding using Causal Mediation Analysis (2024.findings-acl)
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Sitiporn Sae Lim, Can Udomcharoenchaikit, Peerat Limkonchotiwat, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Current NLU models obtain state-of-the-art accuracy on in-distribution benchmarks, but they use annotation bias to make predictions, negatively affecting the models' generalizability. |
| Approach: | They apply causal mediation analysis to gauge how much each component mediates annotation biases and use causal-grounded masking and gradient unlearning to mitigate bias. |
| Outcome: | The proposed methods improve the model's robustness against annotation bias even after employing other training-time debiasing techniques. |
Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval (2021.findings-emnlp)
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Nattapol Trijakwanich, Peerat Limkonchotiwat, Raheem Sarwar, Wannaphong Phatthiyaphaibun, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. |
| Approach: | They propose a framework for cross-lingual sentence retrieval that uses a collection of fragments to improve sentence retrievals. |
| Outcome: | The proposed framework improves the retrieval robustness of the base sentences encoded by m-USE, LASER, and LaBSE. |
MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model (2024.findings-acl)
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| Challenge: | Existing IR techniques contain deficiencies, posing a performance bottleneck . combining diverse approaches to retrieve information is a viable strategy . |
| Approach: | They propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems. |
| Outcome: | The proposed method outperforms existing methods on two Retrieval Question Answering tasks. |
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering (2022.findings-naacl)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing approaches to cross-lingual question answering use sentence embedding to map documents and questions in multiple languages . a novel cross-linguistic approach to cross language-retrieval question answering is proposed . our method outperforms competitors in 19 out of 21 settings of CL-ReQA . |
| Approach: | They propose a cross-lingual language knowledge transfer framework for cross-linguistic question answering . they use a multilingual sentence embedding technique to create a linguistic embeddable space . |
| Outcome: | The proposed method outperforms current state-of-the-art methods in 19 out of 21 settings of CL-ReQA. |
Handling Cross- and Out-of-Domain Samples in Thai Word Segmentation (2021.findings-acl)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Word segmentation is domain-dependent, which can be a challenge in low-resource languages like Thai and Urdu . a framework to handle out-of-domain inputs is proposed to improve word segmentation . |
| Approach: | They propose a domaingeneric domain adaptation framework and data augmentation technique to combat low-resource problems. |
| Outcome: | The proposed model outperforms the state-of-the-art Thai word segmentation method in out-of domain scenarios. |
Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning (2024.emnlp-main)
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Panuthep Tasawong, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Entity disambiguation (ED) is crucial in natural language processing tasks such as question-answering and information extraction. |
| Approach: | They propose a method to reduce computational overhead on overshadowed entities by addressing shortcut learning. |
| Outcome: | The proposed method achieves state-of-the-art performance without compromising inference speed. |
Domain Adaptation of Thai Word Segmentation Models using Stacked Ensemble (2020.emnlp-main)
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Peerat Limkonchotiwat, Wannaphong Phatthiyaphaibun, Raheem Sarwar, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Thai word segmentation is domain-dependent, and researchers have been relying on transfer learning to adapt existing models to new domains. |
| Approach: | They propose a filter-and-refine solution to address Thai word segmentation as a domain-dependent problem. |
| Outcome: | The proposed method is an effective domain adaptation method and has similar performance as the transfer learning method. |