29 citations · 44 across the 15 of their papers we have counts for
15 papers
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations
Shiao Meng, Xuming Hu, Aiwei Liu +4
Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the cont…
FSMR: A Feature Swapping Multi-modal Reasoning Approach with Joint Textual and Visual Clues
Shuang Li, Jiahua Wang, Lijie Wen
Multi-modal reasoning plays a vital role in bridging the gap between textual and visual information, enabling a deeper understanding of the context. This paper presents the Feature…
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
Shiao Meng, Xuming Hu, Aiwei Liu +4
How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for…
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Aiwei Liu, Leyi Pan, Xuming Hu +4
Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyri…
Contrastive Disentangled Learning on Graph for Node Classification
Xiaojuan Zhang, Jun Fu, Shuang Li
Contrastive learning methods have attracted considerable attention due to their remarkable success in analyzing graph-structured data. Inspired by the success of contrastive learni…
Variational Disentangled Graph Auto-Encoders for Link Prediction
Jun Fu, Xiaojuan Zhang, Shuang Li +1
With the explosion of graph-structured data, link prediction has emerged as an increasingly important task. Embedding methods for link prediction utilize neural networks to generat…
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing
Aiwei Liu, Wei Liu, Xuming Hu +4
In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each in…
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer
Shuang Li, Xuming Hu, Aiwei Liu +4
Cross-lingual natural language inference is a fundamental problem in cross-lingual language understanding. Many recent works have used prompt learning to address the lack of annota…
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
Yawen Yang, Xuming Hu, Fukun Ma +4
Named Entity Recognition (NER) is a well and widely studied task in natural language processing. Recently, the nested NER has attracted more attention since its practicality and di…
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution
Aiwei Liu, Honghai Yu, Xuming Hu +5
We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split int…
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Shu'ang Li, Xuming Hu, Li Lin +3
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and…
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
Shuliang Liu, Xuming Hu, Chenwei Zhang +3
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Ex…
Pair-Level Supervised Contrastive Learning for Natural Language Inference
Shu'ang Li, Xuming Hu, Li Lin +1
Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer the relationship between the sentence pair (premi…
What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation
Li Lin, Yixin Cao, Lifu Huang +4
Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challengin…
An Approach for Process Model Extraction By Multi-Grained Text Classification
Chen Qian, Lijie Wen, Akhil Kumar +5
Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process…