56 citations · 68 across the 10 of their papers we have counts for
10 papers
Improving Few-shot and Zero-shot Entity Linking with Coarse-to-Fine Lexicon-based Retriever
Shijue Huang, Bingbing Wang, Libo Qin +2
Few-shot and zero-shot entity linking focus on the tail and emerging entities, which are more challenging but closer to real-world scenarios. The mainstream method is the ''retriev…
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Libo Qin, Shijue Huang, Qiguang Chen +5
Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-m…
A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers
Xiaoyan Zhao, Yang Deng, Min Yang +6
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and inf…
A Diffusion Model for Event Skeleton Generation
Fangqi Zhu, Lin Zhang, Jun Gao +3
Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critic…
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
Xiaokang Liu, Jianquan Li, Jingjing Mu +3
Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential bu…
Exploring the Feasibility of ChatGPT for Event Extraction
Jun Gao, Huan Zhao, Changlong Yu +1
Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challeng…
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
Jun Gao, Changlong Yu, Wei Wang +2
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can genera…
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
Fangqi Zhu, Jun Gao, Changlong Yu +5
Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted t…
Interpretable Proof Generation via Iterative Backward Reasoning
Hanhao Qu, Yu Cao, Jun Gao +2
We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series o…
Empathetic Response Generation with State Management
Yuhan Liu, Jun Gao, Jiachen Du +2
A good empathetic dialogue system should first track and understand a user's emotion and then reply with an appropriate emotion. However, current approaches to this task either foc…