|
| 1 | +--- |
| 2 | +--- |
| 3 | +References |
| 4 | +========== |
| 5 | +
|
| 6 | +
|
| 7 | +@article{gnmt, |
| 8 | + author = {Yonghui Wu and |
| 9 | + Mike Schuster and |
| 10 | + Zhifeng Chen and |
| 11 | + Quoc V. Le and |
| 12 | + Mohammad Norouzi and |
| 13 | + Wolfgang Macherey and |
| 14 | + Maxim Krikun and |
| 15 | + Yuan Cao and |
| 16 | + Qin Gao and |
| 17 | + Klaus Macherey and |
| 18 | + Jeff Klingner and |
| 19 | + Apurva Shah and |
| 20 | + Melvin Johnson and |
| 21 | + Xiaobing Liu and |
| 22 | + Lukasz Kaiser and |
| 23 | + Stephan Gouws and |
| 24 | + Yoshikiyo Kato and |
| 25 | + Taku Kudo and |
| 26 | + Hideto Kazawa and |
| 27 | + Keith Stevens and |
| 28 | + George Kurian and |
| 29 | + Nishant Patil and |
| 30 | + Wei Wang and |
| 31 | + Cliff Young and |
| 32 | + Jason Smith and |
| 33 | + Jason Riesa and |
| 34 | + Alex Rudnick and |
| 35 | + Oriol Vinyals and |
| 36 | + Greg Corrado and |
| 37 | + Macduff Hughes and |
| 38 | + Jeffrey Dean}, |
| 39 | + title = {Google's Neural Machine Translation System: Bridging the Gap between |
| 40 | + Human and Machine Translation}, |
| 41 | + journal = {CoRR}, |
| 42 | + volume = {abs/1609.08144}, |
| 43 | + year = {2016}, |
| 44 | + url = {http://arxiv.org/abs/1609.08144}, |
| 45 | + archivePrefix = {arXiv}, |
| 46 | + eprint = {1609.08144}, |
| 47 | + timestamp = {Thu, 14 Mar 2019 09:34:18 +0100}, |
| 48 | + biburl = {https://dblp.org/rec/journals/corr/WuSCLNMKCGMKSJL16.bib}, |
| 49 | + bibsource = {dblp computer science bibliography, https://dblp.org} |
| 50 | +} |
| 51 | + |
| 52 | +@misc{bahdanau2014neural, |
| 53 | + abstract = {Neural machine translation is a recently proposed approach to machine |
| 54 | +translation. Unlike the traditional statistical machine translation, the neural |
| 55 | +machine translation aims at building a single neural network that can be |
| 56 | +jointly tuned to maximize the translation performance. The models proposed |
| 57 | +recently for neural machine translation often belong to a family of |
| 58 | +encoder-decoders and consists of an encoder that encodes a source sentence into |
| 59 | +a fixed-length vector from which a decoder generates a translation. In this |
| 60 | +paper, we conjecture that the use of a fixed-length vector is a bottleneck in |
| 61 | +improving the performance of this basic encoder-decoder architecture, and |
| 62 | +propose to extend this by allowing a model to automatically (soft-)search for |
| 63 | +parts of a source sentence that are relevant to predicting a target word, |
| 64 | +without having to form these parts as a hard segment explicitly. With this new |
| 65 | +approach, we achieve a translation performance comparable to the existing |
| 66 | +state-of-the-art phrase-based system on the task of English-to-French |
| 67 | +translation. Furthermore, qualitative analysis reveals that the |
| 68 | +(soft-)alignments found by the model agree well with our intuition.}, |
| 69 | + added-at = {2020-06-07T20:24:58.000+0200}, |
| 70 | + author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua}, |
| 71 | + biburl = {https://www.bibsonomy.org/bibtex/2713375898fd7d2477f6ab6dc3dd66c2c/jan.hofmann1}, |
| 72 | + description = {[1409.0473] Neural Machine Translation by Jointly Learning to Align and Translate}, |
| 73 | + interhash = {bb2ca011eeafccb0bd2505c9476dcd10}, |
| 74 | + intrahash = {713375898fd7d2477f6ab6dc3dd66c2c}, |
| 75 | + keywords = {thema:pyramid_scene_parsing}, |
| 76 | + note = {cite arxiv:1409.0473Comment: Accepted at ICLR 2015 as oral presentation}, |
| 77 | + timestamp = {2020-06-07T20:24:58.000+0200}, |
| 78 | + title = {Neural Machine Translation by Jointly Learning to Align and Translate}, |
| 79 | + url = {http://arxiv.org/abs/1409.0473}, |
| 80 | + year = 2014 |
| 81 | +} |
| 82 | + |
| 83 | +@misc{attention, |
| 84 | + title={Attention Is All You Need}, |
| 85 | + author={Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin}, |
| 86 | + year={2017}, |
| 87 | + eprint={1706.03762}, |
| 88 | + archivePrefix={arXiv}, |
| 89 | + primaryClass={cs.CL} |
| 90 | +} |
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