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Edoardo Holzl
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Update bibliography and add latex script
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_bibliography/references.bib

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---
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---
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References
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==========
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@article{gnmt,
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author = {Yonghui Wu and
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Mike Schuster and
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Zhifeng Chen and
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Quoc V. Le and
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Mohammad Norouzi and
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Wolfgang Macherey and
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Maxim Krikun and
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Yuan Cao and
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Qin Gao and
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Klaus Macherey and
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Jeff Klingner and
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Apurva Shah and
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Melvin Johnson and
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Xiaobing Liu and
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Lukasz Kaiser and
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Stephan Gouws and
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Yoshikiyo Kato and
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Taku Kudo and
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Hideto Kazawa and
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Keith Stevens and
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George Kurian and
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Nishant Patil and
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Wei Wang and
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Cliff Young and
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Jason Smith and
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Jason Riesa and
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Alex Rudnick and
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Oriol Vinyals and
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Greg Corrado and
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Macduff Hughes and
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Jeffrey Dean},
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title = {Google's Neural Machine Translation System: Bridging the Gap between
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Human and Machine Translation},
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journal = {CoRR},
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volume = {abs/1609.08144},
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year = {2016},
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url = {http://arxiv.org/abs/1609.08144},
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archivePrefix = {arXiv},
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eprint = {1609.08144},
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timestamp = {Thu, 14 Mar 2019 09:34:18 +0100},
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biburl = {https://dblp.org/rec/journals/corr/WuSCLNMKCGMKSJL16.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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@misc{bahdanau2014neural,
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abstract = {Neural machine translation is a recently proposed approach to machine
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translation. Unlike the traditional statistical machine translation, the neural
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machine translation aims at building a single neural network that can be
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jointly tuned to maximize the translation performance. The models proposed
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recently for neural machine translation often belong to a family of
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encoder-decoders and consists of an encoder that encodes a source sentence into
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a fixed-length vector from which a decoder generates a translation. In this
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paper, we conjecture that the use of a fixed-length vector is a bottleneck in
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improving the performance of this basic encoder-decoder architecture, and
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propose to extend this by allowing a model to automatically (soft-)search for
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parts of a source sentence that are relevant to predicting a target word,
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without having to form these parts as a hard segment explicitly. With this new
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approach, we achieve a translation performance comparable to the existing
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state-of-the-art phrase-based system on the task of English-to-French
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translation. Furthermore, qualitative analysis reveals that the
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(soft-)alignments found by the model agree well with our intuition.},
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added-at = {2020-06-07T20:24:58.000+0200},
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author = {Bahdanau, Dzmitry and Cho, Kyunghyun and Bengio, Yoshua},
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biburl = {https://www.bibsonomy.org/bibtex/2713375898fd7d2477f6ab6dc3dd66c2c/jan.hofmann1},
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description = {[1409.0473] Neural Machine Translation by Jointly Learning to Align and Translate},
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interhash = {bb2ca011eeafccb0bd2505c9476dcd10},
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intrahash = {713375898fd7d2477f6ab6dc3dd66c2c},
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keywords = {thema:pyramid_scene_parsing},
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note = {cite arxiv:1409.0473Comment: Accepted at ICLR 2015 as oral presentation},
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timestamp = {2020-06-07T20:24:58.000+0200},
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title = {Neural Machine Translation by Jointly Learning to Align and Translate},
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url = {http://arxiv.org/abs/1409.0473},
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year = 2014
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}
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@misc{attention,
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title={Attention Is All You Need},
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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},
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year={2017},
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eprint={1706.03762},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}

_config.yml

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kramdown:
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toc_levels: 1..2
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plugins: ['jekyll/scholar']

_debug.yml

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toc_levels: 1..2
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plugins: ['jekyll/scholar']

_layouts/default.html

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</body>
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</html>
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<script type="text/x-mathjax-config">
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MathJax.Hub.Config({
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tex2jax: {
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inlineMath: [['$','$'], ['\\(','\\)']],
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<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>

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