1.2k citations · 1.5k across the 11 of their papers we have counts for
11 papers
Co-Regularized Adversarial Learning for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Multi-domain text classification (MDTC) aims to leverage all available resources from multiple domains to learn a predictive model that can generalize well on these domains. Recent…
Maximum Batch Frobenius Norm for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Multi-domain text classification (MDTC) has obtained remarkable achievements due to the advent of deep learning. Recently, many endeavors are devoted to applying adversarial learni…
Towards Category and Domain Alignment: Category-Invariant Feature Enhancement for Adversarial Domain Adaptation
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Adversarial domain adaptation has made impressive advances in transferring knowledge from the source domain to the target domain by aligning feature distributions of both domains.…
Context-Sensitive Visualization of Deep Learning Natural Language Processing Models
Andrew Dunn, Diana Inkpen, Răzvan Andonie
The introduction of Transformer neural networks has changed the landscape of Natural Language Processing (NLP) during the last years. So far, none of the visualization systems has…
Conditional Adversarial Networks for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
In this paper, we propose conditional adversarial networks (CANs), a framework that explores the relationship between the shared features and the label predictions to impose more d…
Mixup Regularized Adversarial Networks for Multi-Domain Text Classification
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Using the shared-private paradigm and adversarial training has significantly improved the performances of multi-domain text classification (MDTC) models. However, there are two iss…
Dual Mixup Regularized Learning for Adversarial Domain Adaptation
Yuan Wu, Diana Inkpen, Ahmed El-Roby
Recent advances on unsupervised domain adaptation (UDA) rely on adversarial learning to disentangle the explanatory and transferable features for domain adaptation. However, there…
Neural Natural Language Inference Models Enhanced with External Knowledge
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling +2
Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neura…
Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling +3
The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with ne…
Enhanced LSTM for Natural Language Inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling +3
Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (B…
Segmentation Similarity and Agreement
Chris Fournier, Diana Inkpen
We propose a new segmentation evaluation metric, called segmentation similarity (S), that quantifies the similarity between two segmentations as the proportion of boundaries that a…