Papers by Tuan Nguyen
CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction (2024.emnlp-main)
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| Challenge: | Existing deep learning methods require large datasets to achieve high generalizability. |
| Approach: | They propose a framework that enhances deep learning models with clinical rationales derived from medically proficient Large Language Models. |
| Outcome: | The proposed framework outperforms state-of-the-art models on two tasks using two popular EHR datasets by up to 11.2%. |
Exploring Pathological Speech Quality Assessment with ASR-Powered Wav2Vec2 in Data-Scarce Context (2024.lrec-main)
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| Challenge: | Current studies only gain good results on simple tasks such as binary classification due to data scarcity. |
| Approach: | They propose to use the pre-trained Wav2Vec2 architecture for both SSL, and ASR as feature extractor in speech assessment. |
| Outcome: | The proposed system achieves the best results on the HNC dataset using 95 training samples. |
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models (2022.emnlp-main)
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Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
| Challenge: | Pre-trained Transformer models provide robust language representations which can be specialized on various tasks. |
| Approach: | They propose an efficient pruning method based on approximate second-order information that allows pruning weight blocks to be used for pruning. |
| Outcome: | The proposed method is the first to be applied at the BERT scale and significantly pushes the boundaries of the current sparse models with respect to all metrics: model size, inference speed and task accuracy. |
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction (2023.emnlp-main)
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| Challenge: | Emotion recognition is a crucial task for human conversation understanding . multimodal data, e.g., language, voice, and facial expressions, add complexity to the task. |
| Approach: | They propose a relational temporal Graph Neural Network with Auxiliary Cross-Modality Interaction framework that captures conversation-level cross-modality interactions and utterance-level temporal dependencies with modality-specific manner for conversation understanding. |
| Outcome: | The proposed framework captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding. |