Papers by Jun Quan
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)
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| Challenge: | Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient. |
| Approach: | They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve. |
| Outcome: | The proposed approach improves on three SLT datasets conducted under the gloss-free setting. |
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue (D19-1)
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| Challenge: | Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. |
| Approach: | They propose a unified end-to-end Generative Ellipsis and CO-reference Resolution model . the model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . |
| Outcome: | The proposed model can generate a new pragmatically complete user utterance by alternating the generation and copy mode for each user . intrinsic evaluations on the resolution of ellipsis and co-reference show that the model outperforms the baseline model in terms of EM, BLEU and F1 . |
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling (2020.emnlp-main)
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| Challenge: | RiSAWOZ contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues spanning over 12 domains . despite of substantial progress made, there are challenges in creating challenging datasets in terms of size, multiple domains, semantic annotations and complexity. |
| Approach: | They propose a large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations that captures discourse phenomena for task-oriented dialogue modeling. |
| Outcome: | The proposed dataset contains 11.2K human-to-human (H2H) multi-turn semantically annotated dialogues with more than 150K utterances spanning over 12 domains. |
Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs (2020.acl-main)
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| Challenge: | a novel framework for text-based diagnosis of diseases requires appropriate balance between accuracy and interpretability. |
| Approach: | They propose a framework that stacks Bayesian Network Ensembles on top of CNN to build an accurate yet interpretable diagnosis system. |
| Outcome: | The proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable. |
Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning (2026.findings-acl)
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| Challenge: | Existing approaches to RLVR provide sparse supervision since reward arrives only after the full generation is complete. |
| Approach: | They propose a step-level reward system that extracts confidence and correctness and combines them into a Step Potential signal that explicitly estimates reasoning state at each step. |
| Outcome: | The proposed method outperforms existing methods on multiple benchmarks and improves accuracy while reducing response length. |
Modeling Long Context for Task-Oriented Dialogue State Generation (2020.acl-main)
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| Challenge: | Existing approaches to dialogue state tracking are limited to scenarios with infinite slot values and prediction of unseen slot values. |
| Approach: | They propose a multi-task learning model with a simple yet effective utterance tagging technique and a bidirectional language model as an auxiliary task for task-oriented dialogue state generation. |
| Outcome: | The proposed model achieves state-of-the-art accuracy on the MultiWOZ 2.0 dataset. |
Structured Optimal Brain Pruning for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing pruning methods for Large Language Models rely on unstructured pruning or require special hardware to accelerate computation. |
| Approach: | They propose a retraining-free structured pruning method called SoBP . they evaluate the effectiveness of SoBP across 14 models from 3 LLM families . |
| Outcome: | The proposed method outperforms current state-of-the-art pruning methods on 8 datasets. |