Papers by Ho-fung Leung
A Boundary-aware Neural Model for Nested Named Entity Recognition (D19-1)
Copied to clipboard
| Challenge: | Existing methods for named entity recognition ignore nested entities . a boundary-aware neural model can locate entities precisely by detecting boundaries . |
| Approach: | They propose a boundary-aware neural model for nested named entity recognition which leverages entity boundaries to predict entity categorical labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on GENIA dataset . it captures dependencies of entity boundaries and categorical labels, which helps to improve identifying entities. |
IgSEG: Image-guided Story Ending Generation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories. |
| Approach: | They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges. |
| Outcome: | The proposed model outperforms baselines on automatic and human evaluation. |
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. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
Copied to clipboard
| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
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. |
Abstract-level Deductive Reasoning for Pre-trained Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods fine-tune PLMs using the validity label and instance-level reasoning proofs as supervision signals. |
| Approach: | They propose to train PLMs to learn general reasoning patterns rather than instance-level knowledge by predicting the abstract reasoning proof of each sample. |
| Outcome: | The proposed model significantly reduces the impact of learning instance-level knowledge (over 70%) |