82 citations · 450 across the 27 of their papers we have counts for
28 papers
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…
CPM: A Large-scale Generative Chinese Pre-trained Language Model
Zhengyan Zhang, Xu Han, Hao Zhou +22
Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot o…
Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph
Xin Lv, Xu Han, Lei Hou +6
Multi-hop reasoning has been widely studied in recent years to seek an effective and interpretable method for knowledge graph (KG) completion. Most previous reasoning methods are d…
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
Zhiyuan Liu, Yixin Cao, Liangming Pan +2
Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising pe…
Learning Syllogism with Euler Neural-Networks
Tiansi Dong, Chengjiang Li, Christian Bauckhage +3
Traditional neural networks represent everything as a vector, and are able to approximate a subset of logical reasoning to a certain degree. As basic logic relations are better rep…
KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization and Completion
Jie Zhou, Shengding Hu, Xin Lv +6
A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph. The two-view KG provides a testbed for models to "simulate" human'…
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…
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu +4
Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relat…
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
Jifan Yu, Chenyu Wang, Gan Luo +4
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users. Suffering from semantic drift…
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…
Multi-Channel Graph Neural Network for Entity Alignment
Yixin Cao, Zhiyuan Liu, Chengjiang Li +2
Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network mod…
Explainable and Explicit Visual Reasoning over Scene Graphs
Jiaxin Shi, Hanwang Zhang, Juanzi Li
We aim to dismantle the prevalent black-box neural architectures used in complex visual reasoning tasks, into the proposed eXplainable and eXplicit Neural Modules (XNMs), which adv…
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Yixin Cao, Lei Hou, Juanzi Li +4
Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for…
Neural Collective Entity Linking
Yixin Cao, Lei Hou, Juanzi Li +1
Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local c…
Learning to Embed Sentences Using Attentive Recursive Trees
Jiaxin Shi, Lei Hou, Juanzi Li +2
Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks…
Differentiating Concepts and Instances for Knowledge Graph Embedding
Xin Lv, Lei Hou, Juanzi Li +1
Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding met…