Papers by Lingxi Zhang
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression. |
| Approach: | They propose a fine-to- coarse-grained framework for KBQA to ensure generalization and executability of the logical expression. |
| Outcome: | The proposed framework derives new state-of-the-art performance on GrailQA and WebQSP, and runs 4 times faster than baseline. |
A Unified Joint Approach with Topological Context Learning and Rule Augmentation for Knowledge Graph Completion (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge graph completion methods perform simple linear update on relation representation, and only local neighborhood information is aggregated, making it difficult to capture logic semantic between relations and global topological context information. |
| Approach: | They propose a joint approach with Topological Context learning and Rule Augmentation (TCRA) it uses a topological context learning mechanism and a relation rule context learning system . |
| Outcome: | The proposed approach performs better on three benchmark datasets and is widely used in knowledgeintensive applications. |
A Decoupled Multi-Agent Framework for Complex Text Style Transfer (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models for text style transfer struggle with complex styles . existing models perform well on simple styles like sentiment and formality . |
| Approach: | They propose a multi-agent self-check framework that includes a large language model as a planner for disentangling subtasks and expert agents for executing the subtask. |
| Outcome: | The proposed framework significantly improves style strength and content preservation on simple and complex style datasets. |