b'Dinghan Shen'

research

∙ 05/31/2021

HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization

Fine-tuning large pre-trained models with task-specific data has achieve...

10 Jiaao Chen, et al. ∙

research

∙ 01/17/2021

What Makes Good In-Context Examples for GPT-3?

GPT-3 has attracted lots of attention due to its superior performance ac...

0 Jiachang Liu, et al. ∙

research

∙ 11/01/2020

MixKD: Towards Efficient Distillation of Large-scale Language Models

Large-scale language models have recently demonstrated impressive empiri...

0 Kevin J Liang, et al. ∙

research

∙ 10/16/2020

CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding

Data augmentation has been demonstrated as an effective strategy for imp...

0 Yanru Qu, et al. ∙

research

∙ 10/12/2020

Improving Self-supervised Pre-training via a Fully-Explored Masked Language Model

Masked Language Model (MLM) framework has been widely adopted for self-s...

0 Mingzhi Zheng, et al. ∙

research

∙ 10/12/2020

Improving Text Generation with Student-Forcing Optimal Transport

Neural language models are often trained with maximum likelihood estimat...

2 Guoyin Wang, et al. ∙

research

∙ 09/29/2020

A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation

Adversarial training has been shown effective at endowing the learned re...

0 Dinghan Shen, et al. ∙

research

∙ 06/16/2020

Generative Semantic Hashing Enhanced via Boltzmann Machines

Generative semantic hashing is a promising technique for large-scale inf...

0 Lin Zheng, et al. ∙

research

∙ 06/01/2020

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

Learning disentangled representations of natural language is essential f...

5 Pengyu Cheng, et al. ∙

research

∙ 05/04/2020

Improving Adversarial Text Generation by Modeling the Distant Future

Auto-regressive text generation models usually focus on local fluency, a...

0 Ruiyi Zhang, et al. ∙

research

∙ 11/10/2019

Syntax-Infused Transformer and BERT models for Machine Translation and Natural Language Understanding

Attention-based models have shown significant improvement over tradition...

0 Dhanasekar Sundararaman, et al. ∙

research

∙ 10/05/2019

Straight-Through Estimator as Projected Wasserstein Gradient Flow

The Straight-Through (ST) estimator is a widely used technique for back-...

5 Pengyu Cheng, et al. ∙

research

∙ 08/29/2019

Document Hashing with Mixture-Prior Generative Models

Hashing is promising for large-scale information retrieval tasks thanks ...

0 Wei Dong, et al. ∙

research

∙ 06/19/2019

Learning Compressed Sentence Representations for On-Device Text Processing

Vector representations of sentences, trained on massive text corpora, ar...

0 Dinghan Shen, et al. ∙

research

∙ 06/05/2019

Syntax-Infused Variational Autoencoder for Text Generation

We present a syntax-infused variational autoencoder (SIVAE), that integr...

0 Xinyuan Zhang, et al. ∙

research

∙ 06/05/2019

Improving Textual Network Embedding with Global Attention via Optimal Transport

Constituting highly informative network embeddings is an important tool ...

0 Liqun Chen, et al. ∙

research

∙ 03/17/2019

Topic-Guided Variational Autoencoders for Text Generation

We propose a topic-guided variational autoencoder (TGVAE) model for text...

0 Wenlin Wang, et al. ∙

research

∙ 02/01/2019

Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models

Variational autoencoders (VAEs) have received much attention recently as...

0 Dinghan Shen, et al. ∙

research

∙ 01/18/2019

Improving Sequence-to-Sequence Learning via Optimal Transport

Sequence-to-sequence models are commonly trained via maximum likelihood ...

0 Liqun Chen, et al. ∙

research

∙ 11/25/2018

Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation

Vision-language navigation (VLN) is the task of navigating an embodied a...

0 Xin Wang, et al. ∙

research

∙ 11/02/2018

Sequence Generation with Guider Network

Sequence generation with reinforcement learning (RL) has received signif...

0 Ruiyi Zhang, et al. ∙

research

∙ 09/17/2018

Adversarial Text Generation via Feature-Mover's Distance

Generative adversarial networks (GANs) have achieved significant success...

0 Liqun Chen, et al. ∙

research

∙ 08/29/2018

Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment

Network embeddings, which learn low-dimensional representations for each...

0 Dinghan Shen, et al. ∙

research

∙ 05/24/2018

Diffusion Maps for Textual Network Embedding

Textual network embedding leverages rich text information associated wit...

0 Xinyuan Zhang, et al. ∙

research

∙ 05/24/2018

Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Many deep learning architectures have been proposed to model the composi...

0 Dinghan Shen, et al. ∙

research

∙ 05/14/2018

NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

Semantic hashing has become a powerful paradigm for fast similarity sear...

0 Dinghan Shen, et al. ∙

research

∙ 05/10/2018

Joint Embedding of Words and Labels for Text Classification

Word embeddings are effective intermediate representations for capturing...

0 Guoyin Wang, et al. ∙

research

∙ 12/28/2017

Topic Compositional Neural Language Model

We propose a Topic Compositional Neural Language Model (TCNLM), a novel ...

0 Wenlin Wang, et al. ∙

research

∙ 10/01/2017

Video Generation From Text

Generating videos from text has proven to be a significant challenge for...

0 Yitong Li, et al. ∙

research

∙ 09/25/2017

Adaptive Convolutional Filter Generation for Natural Language Understanding

Convolutional neural networks (CNNs) have recently emerged as a popular ...

0 Dinghan Shen, et al. ∙

research

∙ 09/21/2017

Deconvolutional Latent-Variable Model for Text Sequence Matching

A latent-variable model is introduced for text matching, inferring sente...

0 Dinghan Shen, et al. ∙

research

∙ 08/16/2017

Deconvolutional Paragraph Representation Learning

Learning latent representations from long text sequences is an important...

0 Yizhe Zhang, et al. ∙

research

∙ 06/12/2017

Adversarial Feature Matching for Text Generation

The Generative Adversarial Network (GAN) has achieved great success in g...

0 Yizhe Zhang, et al. ∙

Dinghan Shen

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