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...
share
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...
research
∙
11/01/2020
MixKD: Towards Efficient Distillation of Large-scale Language Models
Large-scale language models have recently demonstrated impressive empiri...
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...
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...
research
∙
10/12/2020
Improving Text Generation with Student-Forcing Optimal Transport
Neural language models are often trained with maximum likelihood estimat...
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...
research
∙
06/16/2020
Generative Semantic Hashing Enhanced via Boltzmann Machines
Generative semantic hashing is a promising technique for large-scale inf...
research
∙
06/01/2020
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Learning disentangled representations of natural language is essential f...
research
∙
05/04/2020
Improving Adversarial Text Generation by Modeling the Distant Future
Auto-regressive text generation models usually focus on local fluency, a...
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...
research
∙
10/05/2019
Straight-Through Estimator as Projected Wasserstein Gradient Flow
The Straight-Through (ST) estimator is a widely used technique for back-...
research
∙
08/29/2019
Document Hashing with Mixture-Prior Generative Models
Hashing is promising for large-scale information retrieval tasks thanks ...
research
∙
06/19/2019
Learning Compressed Sentence Representations for On-Device Text Processing
Vector representations of sentences, trained on massive text corpora, ar...
research
∙
06/05/2019
Syntax-Infused Variational Autoencoder for Text Generation
We present a syntax-infused variational autoencoder (SIVAE), that integr...
research
∙
06/05/2019
Improving Textual Network Embedding with Global Attention via Optimal Transport
Constituting highly informative network embeddings is an important tool ...
research
∙
03/17/2019
Topic-Guided Variational Autoencoders for Text Generation
We propose a topic-guided variational autoencoder (TGVAE) model for text...
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...
research
∙
01/18/2019
Improving Sequence-to-Sequence Learning via Optimal Transport
Sequence-to-sequence models are commonly trained via maximum likelihood ...
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...
research
∙
11/02/2018
Sequence Generation with Guider Network
Sequence generation with reinforcement learning (RL) has received signif...
research
∙
09/17/2018
Adversarial Text Generation via Feature-Mover's Distance
Generative adversarial networks (GANs) have achieved significant success...
research
∙
08/29/2018
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Network embeddings, which learn low-dimensional representations for each...
research
∙
05/24/2018
Diffusion Maps for Textual Network Embedding
Textual network embedding leverages rich text information associated wit...
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...
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...
research
∙
05/10/2018
Joint Embedding of Words and Labels for Text Classification
Word embeddings are effective intermediate representations for capturing...
research
∙
12/28/2017
Topic Compositional Neural Language Model
We propose a Topic Compositional Neural Language Model (TCNLM), a novel ...
research
∙
10/01/2017
Video Generation From Text
Generating videos from text has proven to be a significant challenge for...
research
∙
09/25/2017
Adaptive Convolutional Filter Generation for Natural Language Understanding
Convolutional neural networks (CNNs) have recently emerged as a popular ...
research
∙
09/21/2017
Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sente...
research
∙
08/16/2017
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important...
research
∙
06/12/2017
