Papers by Muzhi Li
Momentum Contrastive Pre-training for Question Answering (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extractive Question Answering generate cloze-like queries different from natural questions in syntax structure, which could overfit pre-trained models to simple keyword matching. |
| Approach: | They propose a method to align the answer probability between cloze-like and natural query-passage sample pairs. |
| Outcome: | The proposed method improves on three benchmarking QA datasets on supervised and zero-shot scenarios. |
Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing embedding-based methods rely on triples in the KG, which is vulnerable to specious relation patterns and long-tail entities. |
| Approach: | They propose a context-enriched framework for KGC that uses a large language model to generate potential answers for each query triple. |
| Outcome: | The proposed framework improves on FB15k237 and WN18RR datasets. |
The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods to predict missing type annotations for knowledge graphs use only structural knowledge in the local neighborhood of entities. |
| Approach: | They propose a model for KG Entity Typing that integrates semantic and structural knowledge to infer missing types. |
| Outcome: | The proposed framework outperforms existing state-of-the-art methods in the Knowledge Graph Entity Typing task. |