85 citations · 633 across the 51 of their papers we have counts for
9 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…
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…
Disentangle-based Continual Graph Representation Learning
Xiaoyu Kou, Yankai Lin, Shaobo Liu +3
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However,…
DisenE: Disentangling Knowledge Graph Embeddings
Xiaoyu Kou, Yankai Lin, Yuntao Li +4
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently. However, the existing research is mainl…
CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models
Yusheng Su, Xu Han, Zhengyan Zhang +5
Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) and achieved consisten…
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation
Qiu Ran, Yankai Lin, Peng Li +1
Non-autoregressive neural machine translation (NAT) predicts the entire target sequence simultaneously and significantly accelerates inference process. However, NAT discards the de…
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…
Coreferential Reasoning Learning for Language Representation
Deming Ye, Yankai Lin, Jiaju Du +4
Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of…
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
Xu Han, Tianyu Gao, Yankai Lin +7
Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on rel…