65 citations · 376 across the 32 of their papers we have counts for
32 papers
Open World Classification with Adaptive Negative Samples
Ke Bai, Guoyin Wang, Jiwei Li +5
Open world classification is a task in natural language processing with key practical relevance and impact. Since the open or {\em unknown} category data only manifests in the infe…
PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue
Minsik Oh, Joosung Lee, Jiwei Li +1
Identifying relevant persona or knowledge for conversational systems is critical to grounded dialogue response generation. However, each grounding has been mostly researched in iso…
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Li Liu, Penggang Chen, Xin Li +4
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, achieving…
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
Shuhe Wang, Yuxian Meng, Rongbin Ouyang +4
To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL mode…
Ranking-Enhanced Unsupervised Sentence Representation Learning
Yeon Seonwoo, Guoyin Wang, Changmin Seo +6
Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking. Despite this progress, sentence enc…
Semi-supervised Learning with Deterministic Labeling and Large Margin Projection
Ji Xu, Gang Ren, Yao Xiao +2
The centrality and diversity of the labeled data are very influential to the performance of semi-supervised learning (SSL), but most SSL models select the labeled data randomly. Th…
Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation
Ye Wang, Jingbo Liao, Hong Yu +3
Currently end-to-end deep learning based open-domain dialogue systems remain black box models, making it easy to generate irrelevant contents with data-driven models. Specifically,…
NN-NER: Named Entity Recognition with Nearest Neighbor Search
Shuhe Wang, Xiaoya Li, Yuxian Meng +4
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in this paper, we introduce a nearest…
Multi-granularity Association Learning Framework for on-the-fly Fine-Grained Sketch-based Image Retrieval
Dawei Dai, Xiaoyu Tang, Shuyin Xia +3
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo in a given query sketch. However, its widespread applicability is limited…
An Efficient and Adaptive Granular-ball Generation Method in Classification Problem
Shuyin Xia, Xiaochuan Dai, Guoyin Wang +2
Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. T…
Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage
Shijing Si, Rui Wang, Jedrek Wosik +5
Small and imbalanced datasets commonly seen in healthcare represent a challenge when training classifiers based on deep learning models. So motivated, we propose a novel framework…
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Wenlin Wang, Hongteng Xu, Zhe Gan +6
We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogen…
Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding
Dhanasekar Sundararaman, Vivek Subramanian, Guoyin Wang +4
Attention-based models have shown significant improvement over traditional algorithms in several NLP tasks. The Transformer, for instance, is an illustrative example that generates…
Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods
Kevin J Liang, Guoyin Wang, Yitong Li +2
We investigate time-dependent data analysis from the perspective of recurrent kernel machines, from which models with hidden units and gated memory cells arise naturally. By consid…
Zero-Shot Recognition via Optimal Transport
Wenlin Wang, Hongteng Xu, Guoyin Wang +2
We propose an optimal transport (OT) framework for generalized zero-shot learning (GZSL), seeking to distinguish samples for both seen and unseen classes, with the assist of auxili…
Improving Textual Network Learning with Variational Homophilic Embeddings
Wenlin Wang, Chenyang Tao, Zhe Gan +7
The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensi…
Learning Word Embeddings with Domain Awareness
Guoyin Wang, Yan Song, Yue Zhang +1
Word embeddings are traditionally trained on a large corpus in an unsupervised setting, with no specific design for incorporating domain knowledge. This can lead to unsatisfactory…
Improving Textual Network Embedding with Global Attention via Optimal Transport
Liqun Chen, Guoyin Wang, Chenyang Tao +6
Constituting highly informative network embeddings is an important tool for network analysis. It encodes network topology, along with other useful side information, into low-dimens…
Discriminative Clustering for Robust Unsupervised Domain Adaptation
Rui Wang, Guoyin Wang, Ricardo Henao
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source datase…
Topic-Guided Variational Autoencoders for Text Generation
Wenlin Wang, Zhe Gan, Hongteng Xu +5
We propose a topic-guided variational autoencoder (TGVAE) model for text generation. Distinct from existing variational autoencoder (VAE) based approaches, which assume a simple Ga…
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li, Ke Bai, Jianqiao Li +3
We investigate adversarial learning in the case when only an unnormalized form of the density can be accessed, rather than samples. With insights so garnered, adversarial learning…
Mining Maximal Dynamic Spatial Co-Location Patterns
Xin Hu, Guoyin Wang, Jiangli Duan
A spatial co-location pattern represents a subset of spatial features whose instances are prevalently located together in a geographic space. Although many algorithms of mining spa…
Generative Adversarial Network Training is a Continual Learning Problem
Kevin J Liang, Chunyuan Li, Guoyin Wang +1
Generative Adversarial Networks (GANs) have proven to be a powerful framework for learning to draw samples from complex distributions. However, GANs are also notoriously difficult…
Sequence Generation with Guider Network
Ruiyi Zhang, Changyou Chen, Zhe Gan +5
Sequence generation with reinforcement learning (RL) has received significant attention recently. However, a challenge with such methods is the sparse-reward problem in the RL trai…