274 citations · 891 across the 147 of their papers we have counts for
19 papers · 1 filter
Sharpness-Aware Minimization with Dynamic Reweighting
Wenxuan Zhou, Fangyu Liu, Huan Zhang +1
Deep neural networks are often overparameterized and may not easily achieve model generalization. Adversarial training has shown effectiveness in improving generalization by regula…
Contextualized Scene Imagination for Generative Commonsense Reasoning
PeiFeng Wang, Jonathan Zamora, Junfeng Liu +3
Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) s…
Structure-Aware Label Smoothing for Graph Neural Networks
Yiwei Wang, Yujun Cai, Yuxuan Liang +5
Representing a label distribution as a one-hot vector is a common practice in training node classification models. However, the one-hot representation may not adequately reflect th…
Prix-LM: Pretraining for Multilingual Knowledge Base Construction
Wenxuan Zhou, Fangyu Liu, Ivan Vulić +2
Knowledge bases (KBs) contain plenty of structured world and commonsense knowledge. As such, they often complement distributional text-based information and facilitate various down…
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Mingyu Derek Ma, Muhao Chen, Te-Lin Wu +1
Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works atte…
Salience-Aware Event Chain Modeling for Narrative Understanding
Xiyang Zhang, Muhao Chen, Jonathan May
Storytelling, whether via fables, news reports, documentaries, or memoirs, can be thought of as the communication of interesting and related events that, taken together, form a con…
Learning Constraints and Descriptive Segmentation for Subevent Detection
Haoyu Wang, Hongming Zhang, Muhao Chen +1
Event mentions in text correspond to real-world events of varying degrees of granularity. The task of subevent detection aims to resolve this granularity issue, recognizing the mem…
Table-based Fact Verification with Salience-aware Learning
Fei Wang, Kexuan Sun, Jay Pujara +2
Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular…
Do Language Models Perform Generalizable Commonsense Inference?
Peifeng Wang, Filip Ilievski, Muhao Chen +1
Inspired by evidence that pretrained language models (LMs) encode commonsense knowledge, recent work has applied LMs to automatically populate commonsense knowledge graphs (CKGs).…
Knowing the No-match: Entity Alignment with Dangling Cases
Zequn Sun, Muhao Chen, Wei Hu
This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alig…
Retrieving Complex Tables with Multi-Granular Graph Representation Learning
Fei Wang, Kexuan Sun, Muhao Chen +2
The task of natural language table retrieval (NLTR) seeks to retrieve semantically relevant tables based on natural language queries. Existing learning systems for this task often…
Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning
Xuelu Chen, Michael Boratko, Muhao Chen +3
Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for ea…
Contrastive Out-of-Distribution Detection for Pretrained Transformers
Wenxuan Zhou, Fangyu Liu, Muhao Chen
Pretrained Transformers achieve remarkable performance when training and test data are from the same distribution. However, in real-world scenarios, the model often faces out-of-di…
PaCo: Preconditions Attributed to Commonsense Knowledge
Ehsan Qasemi, Filip Ilievski, Muhao Chen +1
Humans can seamlessly reason with circumstantial preconditions of commonsense knowledge. We understand that a glass is used for drinking water, unless the glass is broken or the wa…
Learning from Noisy Labels for Entity-Centric Information Extraction
Wenxuan Zhou, Muhao Chen
Recent information extraction approaches have relied on training deep neural models. However, such models can easily overfit noisy labels and suffer from performance degradation. W…
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
Junheng Hao, Muhao Chen, Wenchao Yu +2
Many large-scale knowledge bases simultaneously represent two views of knowledge graphs (KGs): an ontology view for abstract and commonsense concepts, and an instance view for spec…
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
Junheng Hao, Chelsea Ju, Muhao Chen +3
The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limit…
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis
Changping Meng, Muhao Chen, Jie Mao +1
Analyzing the readability of articles has been an important sociolinguistic task. Addressing this task is necessary to the automatic recommendation of appropriate articles to reade…
An Improved Baseline for Sentence-level Relation Extraction
Wenxuan Zhou, Muhao Chen
Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best perf…