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seq2seqapps.bib
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@inproceedings{Bahdanau2015,
abstract = {Neural machine translation is a recently proposed approach to machine transla-tion. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neu-ral machine translation often belong to a family of encoder–decoders and encode a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder–decoder architec-ture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.},
archivePrefix = {arXiv},
arxivId = {1409.0473},
author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
booktitle = {Iclr 2015},
doi = {10.1146/annurev.neuro.26.041002.131047},
eprint = {1409.0473},
isbn = {0147-006X (Print)},
issn = {0147-006X},
keywords = {Neural machine translation is a recently proposed,Unlike the traditional statistical machine transla,a source sentence into a fixed-length vector from,and propose to extend this by allowing a model to,bottleneck in improving the performance of this ba,for parts of a source sentence that are relevant t,having to form these parts as a hard segment expli,machine translation often belong to a family of en,maximize the translation performance. The models p,phrase-based system on the task of English-to-Fren,qualitative analysis reveals that the (soft-)align,the neural machine,translation aims at building a single neural netwo,translation. In this paper,we achieve a translation performance comparable to,we conjecture that the use of a fixed-length vecto,well with our intuition,without},
pages = {1--15},
pmid = {14527267},
title = {{Neural Machine Translation By Jointly Learning To Align and Translate}},
url = {http://arxiv.org/abs/1409.0473 http://arxiv.org/abs/1409.0473v3},
year = {2014}
}
@article{Chorowski2015,
author = {Chorowski, JK and Bahdanau, D and Serdyuk, D},
journal = {Advances in Neural},
title = {{Attention-based models for speech recognition}},
url = {http://papers.nips.cc/paper/5847-attention-based-models-for-speech-recognition},
year = {2015}
}
@inproceedings{filippova15sentence,
abstract = {利用lstm的sentence to sentence 框架进行句子压缩},
author = {Filippova, Katja and Alfonseca, Enrique and Colmenares, Carlos A and Kaiser, Lukasz and Vinyals, Oriol},
booktitle = {Emnlp},
isbn = {9781941643327},
number = {September},
pages = {360--368},
title = {{Sentence Compression by Deletion with LSTMs}},
volume = {利用lstm的sen},
year = {2015}
}
@article{graves13generating,
abstract = {This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time. The approach is demonstrated for text (where the data are discrete) and online handwriting (where the data are real-valued). It is then extended to handwriting synthesis by allowing the network to condition its predictions on a text sequence. The resulting system is able to generate highly realistic cursive handwriting in a wide variety of styles.},
archivePrefix = {arXiv},
arxivId = {arXiv:1308.0850v5},
author = {Graves, Alex},
doi = {10.1145/2661829.2661935},
eprint = {arXiv:1308.0850v5},
isbn = {2000201075},
issn = {18792782},
journal = {arXiv preprint arXiv:1308.0850},
pages = {1--43},
pmid = {23459267},
title = {{Generating sequences with recurrent neural networks}},
url = {http://arxiv.org/abs/1308.0850},
volume = {abs/1308.0},
year = {2013}
}
@article{Hermann2015,
author = {Hermann, KM and Kocisky, T and Grefenstette, E},
journal = {Advances in Neural},
title = {{Teaching machines to read and comprehend}},
url = {http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
year = {2015}
}
@inproceedings{Jean2015,
author = {Jean, Sebastien and Cho, Kyunghyun and Memisevic, Roland and Bengio, Yoshua},
booktitle = {Proceedings of ACL},
title = {{On Using Very Large Target Vocabulary for Neural Machine Translation}},
year = {2015}
}
@inproceedings{Kalchbrenner2013,
abstract = {We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is {\textgreater} 43{\%} lower than that of state-of-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and mean- ing of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.},
archivePrefix = {arXiv},
arxivId = {1409.0473},
author = {Kalchbrenner, Nal and Blunsom, Phil},
booktitle = {Emnlp},
eprint = {1409.0473},
isbn = {9781937284978},
number = {October},
pages = {1700--1709},
title = {{Recurrent Continuous Translation Models}},
year = {2013}
}
@inproceedings{sutskever14sequence,
abstract = {Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT'14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set, where the LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty on long sentences. For comparison, a phrase-based SMT system achieves a BLEU score of 33.3 on the same dataset. When we used the LSTM to rerank the 1000 hypotheses produced by the aforementioned SMT system, its BLEU score increases to 36.5, which is close to the previous best result on this task. The LSTM also learned sensible phrase and sentence representations that are sensitive to word order and are relatively invariant to the active and the passive voice. Finally, we found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.},
archivePrefix = {arXiv},
arxivId = {1409.3215},
author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.},
booktitle = {Nips},
eprint = {1409.3215},
isbn = {1409.3215},
pages = {9},
pmid = {2079951},
title = {{Sequence to Sequence Learning with Neural Networks}},
url = {http://arxiv.org/abs/1409.3215},
year = {2014}
}
@article{Vinyals2015,
author = {Vinyals, O and Le, Q},
file = {::},
journal = {arXiv preprint arXiv:1506.05869},
title = {{A neural conversational model}},
url = {http://arxiv.org/abs/1506.05869},
year = {2015}
}
@inproceedings{vinyals15grammar,
abstract = {Syntactic parsing is a fundamental problem in computational linguistics and nat-ural language processing. Traditional approaches to parsing are highly complex and problem specific. Recently, Sutskever et al. (2014) presented a task-agnostic method for learning to map input sequences to output sequences that achieved strong results on a large scale machine translation problem. In this work, we show that precisely the same sequence-to-sequence method achieves results that are close to state-of-the-art on syntactic constituency parsing, whilst making al-most no assumptions about the structure of the problem. To achieve these results we need to mitigate the lack of domain knowledge in the model by providing it with a large amount of automatically parsed data.},
archivePrefix = {arXiv},
arxivId = {arXiv:1412.7449v3},
author = {Vinyals, Oriol and Kaiser, Lukasz and Koo, Terry and Petrov, Slav and Sutskever, Ilya and Hinton, Geoffrey},
booktitle = {arXiv},
doi = {10.1146/annurev.neuro.26.041002.131047},
eprint = {arXiv:1412.7449v3},
isbn = {9789078328414},
issn = {10495258},
pages = {1--10},
pmid = {14527267},
title = {{Grammar as a Foreign Language}},
year = {2014}
}
@article{Xu2015,
abstract = {Inspired by recent work in machine translation and object detection, we introduce an attention based model that automatically learns to describe the content of images. We describe how we can train this model in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound. We also show through visualization how the model is able to automatically learn to fix its gaze on salient objects while generating the corresponding words in the output sequence. We validate the use of attention with state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.},
archivePrefix = {arXiv},
arxivId = {1502.03044},
author = {Xu, Kelvin and Ba, Jimmy and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and Salakhutdinov, Ruslan and Zemel, Richard and Bengio, Yoshua},
eprint = {1502.03044},
file = {:home/srush/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Xu et al. - 2015 - Show, Attend and Tell Neural Image Caption Generation with Visual Attention(2).pdf:pdf},
month = {feb},
title = {{Show, Attend and Tell: Neural Image Caption Generation with Visual Attention}},
url = {http://arxiv.org/abs/1502.03044},
year = {2015}
}
@article{Venugopalan2015,
author = {Venugopalan, S and Rohrbach, M and Donahue, J},
journal = {Proceedings of the},
title = {{Sequence to sequence-video to text}},
url = {http://www.cv-foundation.org/openaccess/content{\_}iccv{\_}2015/html/Venugopalan{\_}Sequence{\_}to{\_}Sequence{\_}ICCV{\_}2015{\_}paper.html},
year = {2015}
}
@article{Wang,
abstract = {We study the problem of generating abstrac-tive summaries for opinionated text. We pro-pose an attention-based neural network model that is able to absorb information from multi-ple text units to construct informative, concise, and fluent summaries. An importance-based sampling method is designed to allow the en-coder to integrate information from an impor-tant subset of input. Automatic evaluation in-dicates that our system outperforms state-of-the-art abstractive and extractive summariza-tion systems on two newly collected datasets of movie reviews and arguments. Our system summaries are also rated as more informative and grammatical in human evaluation.},
author = {Wang, Lu and Ling, Wang},
file = {::},
title = {{Neural Network-Based Abstract Generation for Opinions and Arguments}}
}