Thang Luong

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Yoshua Bengio24
Christopher D. Manning22
Tomas Mikolov20
Kyunghyun Cho16
Dzmitry Bahdanau16
16Christopher D. Manning
14Chris Dyer
12Minh-Thang Luong
11Zhiting Hu
11Qun Liu

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Effective Approaches to Attention-based Neural Machine Translation
An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful architectures for attention-based NMT. This paper examines two simple and effective classes of at-tentional mechanism: a global approach… (More)
Addressing the Rare Word Problem in Neural Machine Translation
Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that… (More)
Better Word Representations with Recursive Neural Networks for Morphology
Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly… (More)
Bilingual Word Representations with Monolingual Quality in Mind
Recent work in learning bilingual representations tend to tailor towards achieving good performance on bilingual tasks, most often the crosslingual document classification (CLDC) evaluation, but to the detriment of preserving clustering structures of word representations monolin-gually. In this work, we propose a joint model to learn word representations… (More)
When Are Tree Structures Necessary for Deep Learning of Representations?
Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. But there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper we benchmark recursive neural models against sequential recurrent neural models (simple… (More)
Learning Distributed Representations for Multilingual Text Sequences
We propose a novel approach to learning distributed representations of variable-length text sequences in multiple languages simultaneously. Unlike previous work which often derive representations of multi-word sequences as weighted sums of individual word vectors , our model learns distributed representations for phrases and sentences as a whole. Our work… (More)
Online and Linear-Time Attention by Enforcing Monotonic Alignments
Recurrent neural network models with an attention mechanism have proven to be extremely effective on a wide variety of sequence-to-sequence problems. However, the fact that soft attention mechanisms perform a pass over the entire input sequence when producing each element in the output sequence precludes their use in online settings and results in a… (More)
Deep Neural Language Models for Machine Translation
Neural language models (NLMs) have been able to improve machine translation (MT) thanks to their ability to generalize well to long contexts. Despite recent successes of deep neural networks in speech and vision, the general practice in MT is to incorporate NLMs with only one or two hidden layers and there have not been clear results on whether having more… (More)
18F-FDG PET/CT appearance of metastatic brachial plexopathy involving epidural space from breast carcinoma.
Patient is a 74-year-old woman with a history of breast carcinoma and known metastatic brachial plexopathy and pulmonary and mediastinal nodal involvement. She had bilateral mastectomies 5 years ago and is currently on chemotherapy. A 6-month follow-up PET/CT study demonstrated no significant interval change in intensity of the previously evident… (More)
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