Papers by Truong Do

3 papers
CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding (2023.eacl-demo)

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Challenge: COVID-19 has affected all aspects of human life, causing problems related to acronyms, synonyms, and rare keywords.
Approach: They propose a hybrid relation retrieval system based on embeddings to provide high-quality search results.
Outcome: The proposed system can be accessed through the following URL: http://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/.
StructSP: Efficient Fine-tuning of Task-Oriented Dialog System by Using Structure-aware Boosting and Grammar Constraints (2023.findings-acl)

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Challenge: Existing models that learn hierarchical structure information representations do not perform well on task-oriented dialog systems.
Approach: They propose a hierarchical structure information representation model that reinforces the semantic awareness of a pre-trained language model by a two-step fine-tuning mechanism.
Outcome: The proposed model is better than existing models at learning the contextual representations of utterances embedded within its hierarchical semantic structure and improves system performance.
HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts (2023.emnlp-main)

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Challenge: Recent studies suggest that fixing the routers can achieve competitive performance by alleviating the collapsing problem, where all experts eventually learn similar representations.
Approach: They propose a method that dynamically generates router parameters through a fixed hypernetwork and trainable embeddings to achieve a balance between training the routers and freezing them to learn an improved routing policy.
Outcome: Experiments on a wide range of tasks show that the proposed method performs better than existing methods.

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