165 citations · 377 across the 46 of their papers we have counts for
7 papers · 1 filter
ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning
Yujia Qin, Yankai Lin, Ryuichi Takanobu +6
Pre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do n…
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade
Lei Li, Yankai Lin, Deli Chen +4
Dynamic early exiting aims to accelerate the inference of pre-trained language models (PLMs) by emitting predictions in internal layers without passing through the entire model. In…
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification
Deli Chen, Yankai Lin, Lei Li +4
Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC). However, existing GCL methods are generally…
Learning from Context or Names? An Empirical Study on Neural Relation Extraction
Hao Peng, Tianyu Gao, Xu Han +5
Neural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding which type of information affects existing RE models…
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection
Yingxue Zhang, Fandong Meng, Peng Li +2
As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the q…
Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions
Bojan Karlaš, Peng Li, Renzhi Wu +4
Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubi…
MAVEN: A Massive General Domain Event Detection Dataset
Xiaozhi Wang, Ziqi Wang, Xu Han +7
Event detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text…