Papers by Son Tran

3 papers
VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding (2024.findings-naacl)

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Challenge: a lack of standard evaluation metrics and benchmarks makes it difficult to identify strengths of Vietnamese NLP models.
Approach: They propose to establish a standardized set of benchmarks for Vietnamese NLU . they propose to evaluate Vietnamese language understanding models using a pre-trained model .
Outcome: The proposed model combines proficiency of a multilingual pre-trained model with Vietnamese linguistic knowledge.
ViLL-E: Video LLM Embeddings for Retrieval (2026.acl-long)

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Challenge: Video Large Language Models excel at video understanding tasks where outputs are textual . however, they underperform specialized embedding-based models in Retrieval tasks .
Approach: They propose a video-LLM-based model with an embedding generation mechanism that allows the model to "think longer" for complex videos and stop early for easy ones.
Outcome: The proposed model outperforms specialized embedding-based models in video understanding tasks while remaining competitive on VideoQA tasks.
Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology (2024.findings-emnlp)

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Challenge: Existing models lack robustness against distribution shifts and adversarial attacks when training on unanswerable questions in EQA datasets.
Approach: They propose a novel loss function for the EQA problem to improve the robustness of extractive question answering models by adding adversarial questions to a crowdsourcing process.
Outcome: The proposed method maintains in-domain performance while improving on out-of-domain datasets.

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