Minh-Thang Luong | DeepAI

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∙ 11/19/2021

Combined Scaling for Zero-shot Transfer Learning

We present a combined scaling method called BASIC that achieves 85.7 zer...

0 Hieu Pham, et al. ∙

∙ 09/13/2021

STraTA: Self-Training with Task Augmentation for Better Few-shot Learning

Despite their recent successes in tackling many NLP tasks, large-scale p...

0 Tu Vu, et al. ∙

∙ 12/15/2020

Pre-Training Transformers as Energy-Based Cloze Models

We introduce Electric, an energy-based cloze model for representation le...

0 Kevin Clark, et al. ∙

∙ 11/09/2020

Towards Domain-Agnostic Contrastive Learning

Despite recent success, most contrastive self-supervised learning method...

0 Vikas Verma, et al. ∙

∙ 03/23/2020

ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

Masked language modeling (MLM) pre-training methods such as BERT corrupt...

0 Kevin Clark, et al. ∙

∙ 01/27/2020

Towards a Human-like Open-Domain Chatbot

We present Meena, a multi-turn open-domain chatbot trained end-to-end on...

16 Daniel Adiwardana, et al. ∙

∙ 11/19/2019

A Hybrid Morpheme-Word Representation for Machine Translation of Morphologically Rich Languages

We propose a language-independent approach for improving statistical mac...

0 Minh-Thang Luong, et al. ∙

∙ 11/11/2019

Self-training with Noisy Student improves ImageNet classification

We present a simple self-training method that achieves 87.4 on ImageNet,...

36 Qizhe Xie, et al. ∙

∙ 10/29/2019

Findings of the Third Workshop on Neural Generation and Translation

This document describes the findings of the Third Workshop on Neural Gen...

0 Hiroaki Hayashi, et al. ∙

∙ 07/10/2019

BAM! Born-Again Multi-Task Networks for Natural Language Understanding

It can be challenging to train multi-task neural networks that outperfor...

0 Kevin Clark, et al. ∙

∙ 06/07/2019

Selfie: Self-supervised Pretraining for Image Embedding

We introduce a pretraining technique called Selfie, which stands for SEL...

2 Trieu H. Trinh, et al. ∙

∙ 04/29/2019

Unsupervised Data Augmentation

Despite its success, deep learning still needs large labeled datasets to...

18 Qizhe Xie, et al. ∙

∙ 09/22/2018

Semi-Supervised Sequence Modeling with Cross-View Training

Unsupervised representation learning algorithms such as word2vec and ELM...

0 Kevin Clark, et al. ∙

∙ 09/19/2018

Latent Topic Conversational Models

Latent variable models have been a preferred choice in conversational mo...

0 Tsung-Hsien Wen, et al. ∙

∙ 06/08/2018

Findings of the Second Workshop on Neural Machine Translation and Generation

This document describes the findings of the Second Workshop on Neural Ma...

0 Alexandra Birch, et al. ∙

∙ 04/23/2018

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

Current end-to-end machine reading and question answering (Q&A) models a...

0 Adams Wei Yu, et al. ∙

∙ 10/05/2017

On the Effective Use of Pretraining for Natural Language Inference

Neural networks have excelled at many NLP tasks, but there remain open q...

0 Ignacio Cases, et al. ∙

∙ 07/01/2017

Efficient Attention using a Fixed-Size Memory Representation

The standard content-based attention mechanism typically used in sequenc...

0 Denny Britz, et al. ∙

∙ 04/03/2017

Online and Linear-Time Attention by Enforcing Monotonic Alignments

Recurrent neural network models with an attention mechanism have proven ...

0 Colin Raffel, et al. ∙

∙ 03/11/2017

Massive Exploration of Neural Machine Translation Architectures

Neural Machine Translation (NMT) has shown remarkable progress over the ...

0 Denny Britz, et al. ∙

∙ 06/29/2016

Compression of Neural Machine Translation Models via Pruning

Neural Machine Translation (NMT), like many other deep learning domains,...

0 Abigail See, et al. ∙

∙ 04/04/2016

Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models

Nearly all previous work on neural machine translation (NMT) has used qu...

0 Minh-Thang Luong, et al. ∙

∙ 11/19/2015

Multi-task Sequence to Sequence Learning

Sequence to sequence learning has recently emerged as a new paradigm in ...

0 Minh-Thang Luong, et al. ∙

∙ 08/17/2015

Effective Approaches to Attention-based Neural Machine Translation

An attentional mechanism has lately been used to improve neural machine ...

0 Minh-Thang Luong, et al. ∙

∙ 02/28/2015

When Are Tree Structures Necessary for Deep Learning of Representations?

Recursive neural models, which use syntactic parse trees to recursively ...

0 Jiwei Li, et al. ∙

∙ 10/30/2014

Addressing the Rare Word Problem in Neural Machine Translation

Neural Machine Translation (NMT) is a new approach to machine translatio...

0 Minh-Thang Luong, et al. ∙

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