10.8k citations · 17.5k across the 28 of their papers we have counts for
7 papers · 1 filter
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
Caglar Gulcehre, Francis Dutil, Adam Trischler +1
We investigate the integration of a planning mechanism into an encoder-decoder architecture with an explicit alignment for character-level machine translation. We develop a model t…
Machine Comprehension by Text-to-Text Neural Question Generation
Xingdi Yuan, Tong Wang, Caglar Gulcehre +5
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervis…
Pointing the Unknown Words
Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati +2
The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the d…
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Iulian Vlad Serban, Alberto García-Durán, Caglar Gulcehre +4
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scal…
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos +2
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two di…
On Using Monolingual Corpora in Neural Machine Translation
Caglar Gulcehre, Orhan Firat, Kelvin Xu +6
Recent work on end-to-end neural network-based architectures for machine translation has shown promising results for En-Fr and En-De translation. Arguably, one of the major factors…
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre +4
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a…