82 citations · 450 across the 27 of their papers we have counts for
9 papers · 1 filter
Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion
Yixin Cao, Xiang Ji, Xin Lv +3
We present InferWiki, a Knowledge Graph Completion (KGC) dataset that improves upon existing benchmarks in inferential ability, assumptions, and patterns. First, each testing sampl…
Prompt-Learning for Fine-Grained Entity Typing
Ning Ding, Yulin Chen, Xu Han +6
As an effective approach to tune pre-trained language models (PLMs) for specific tasks, prompt-learning has recently attracted much attention from researchers. By using \textit{clo…
Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification
Shengding Hu, Ning Ding, Huadong Wang +5
Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification. Particularly, previous studies suggest that prompt-tunin…
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition
Meihan Tong, Shuai Wang, Bin Xu +4
Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions. Prototypical network shows superior performance on fe…
TWAG: A Topic-Guided Wikipedia Abstract Generator
Fangwei Zhu, Shangqing Tu, Jiaxin Shi +3
Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, pr…
Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making
Zijun Yao, Chengjiang Li, Tiansi Dong +6
Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entiti…
CLEVE: Contrastive Pre-training for Event Extraction
Ziqi Wang, Xiaozhi Wang, Xu Han +6
Event extraction (EE) has considerably benefited from pre-trained language models (PLMs) by fine-tuning. However, existing pre-training methods have not involved modeling event cha…
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph
Jiaxin Shi, Shulin Cao, Lei Hou +2
Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represent…
Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability
Xin Lv, Yixin Cao, Lei Hou +4
Multi-hop reasoning has been widely studied in recent years to obtain more interpretable link prediction. However, we find in experiments that many paths given by these models are…