135 citations · 868 across the 76 of their papers we have counts for
9 papers · 1 filter
Adversarial Language Games for Advanced Natural Language Intelligence
Yuan Yao, Haoxi Zhong, Zhengyan Zhang +6
We study the problem of adversarial language games, in which multiple agents with conflicting goals compete with each other via natural language interactions. While adversarial lan…
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
Tianyu Gao, Xu Han, Hao Zhu +4
We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of inst…
OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction
Xu Han, Tianyu Gao, Yuan Yao +3
OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE). Specifically, by implementing typical RE…
Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations
Xin Lv, Yuxian Gu, Xu Han +3
Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task. Most previous met…
Neural Snowball for Few-Shot Relation Learning
Tianyu Gao, Xu Han, Ruobing Xie +4
Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient trai…
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
Jie Zhou, Xu Han, Cheng Yang +4
Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simult…
Quantifying Similarity between Relations with Fact Distribution
Weize Chen, Hao Zhu, Xu Han +2
We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence betwe…
ERNIE: Enhanced Language Representation with Informative Entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu +3
Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently imp…
DocRED: A Large-Scale Document-Level Relation Extraction Dataset
Yuan Yao, Deming Ye, Peng Li +7
Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on…