Papers by Shun Wu
Named Entity Recognition via Noise Aware Training Mechanism with Data Filter (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition (NER) do not distinguish noisy from hard samples. |
| Approach: | They propose a noise-aware-with-filter method to help model identify noisy samples . they propose 'incomplete trust' loss function which boosts L CRF with a robust term . |
| Outcome: | The proposed method outperforms the existing methods on six real-world Chinese and English NER datasets. |
Unsupervised Multi-scale Expressive Speaking Style Modeling with Hierarchical Context Information for Audiobook Speech Synthesis (2022.coling-1)
Copied to clipboard
| Challenge: | a recent study has shown that expressiveness of audiobooks is limited by the averaged global-scale speaking style representation. |
| Approach: | They propose an unsupervised multi-scale context-sensitive text-to-speech model for audiobooks . they use hierarchical context encoder to predict global-scale contextual style embeddings . |
| Outcome: | The proposed model outperforms existing models on a real-world Mandarin audio dataset. |
MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluation methods focus on fluency and factual reliability, while neglecting figurative quality. |
| Approach: | They propose a set of human evaluation metrics focused on the translation of figurative language and a parallel metaphor corpus generated by post-editing. |
| Outcome: | The proposed evaluation protocol estimates four aspects of MT: Metaphorical Equivalence, Emotion, Authenticity, and Quality. |
KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large Language Model (2024.emnlp-demo)
Copied to clipboard
| Challenge: | Existing Knowledge-Enhanced Large Language Models (K-LLMs) toolkits focus on free-textual knowledge and lack robust datasets, models, and user-friendly experience. |
| Approach: | They propose a flexible heterogeneous knowledge enhancement toolkit to enhance Large Language Models (LLMs) using external knowledge. |
| Outcome: | KMatrix: a flexible heterogeneous knowledge enhancement toolkit for LLMs includes verbalizing-retrieval and parsing-query methods. |
KMatrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model (2025.emnlp-demos)
Copied to clipboard
Shun Wu, Di Wu, Wangtao Sun, Ziyang Huang, Xiaowei Yuan, Kun Luo, XueYou Zhang, Shizhu He, Jun Zhao, Kang Liu
| Challenge: | Existing studies on K-LLMs systems focus on declarative knowledge and procedural knowledge (rules) . |
| Approach: | They propose to build a toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). |
| Outcome: | The proposed toolkit provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. |