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@@ -1,7 +1,7 @@
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# SyntaxNet: Neural Models of Syntax.
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*A TensorFlow implementation of the models described in [Andor et al. (2016)]
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-(http://arxiv.org/pdf/1603.06042v1.pdf).*
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+(http://arxiv.org/abs/1603.06042).*
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**Update**: Parsey models are now [available](universal.md) for 40 languages
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trained on Universal Dependencies datasets, with support for text segmentation
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@@ -29,13 +29,13 @@ Model
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[Martins et al. (2013)](http://www.cs.cmu.edu/~ark/TurboParser/) | 93.10 | 88.23 | 94.21
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[Zhang and McDonald (2014)](http://research.google.com/pubs/archive/38148.pdf) | 93.32 | 88.65 | 93.37
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[Weiss et al. (2015)](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf) | 93.91 | 89.29 | 94.17
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-[Andor et al. (2016)](http://arxiv.org/pdf/1603.06042v1.pdf)* | 94.44 | 90.17 | 95.40
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+[Andor et al. (2016)](http://arxiv.org/abs/1603.06042)* | 94.44 | 90.17 | 95.40
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Parsey McParseface | 94.15 | 89.08 | 94.77
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We see that Parsey McParseface is state-of-the-art; more importantly, with
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SyntaxNet you can train larger networks with more hidden units and bigger beam
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sizes if you want to push the accuracy even further: [Andor et al. (2016)]
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-(http://arxiv.org/pdf/1603.06042v1.pdf)* is simply a SyntaxNet model with a
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+(http://arxiv.org/abs/1603.06042)* is simply a SyntaxNet model with a
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larger beam and network. For futher information on the datasets, see that paper
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under the section "Treebank Union".
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@@ -45,7 +45,7 @@ Parsey McParseface is also state-of-the-art for part-of-speech (POS) tagging
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Model | News | Web | Questions
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-------------------------------------------------------------------------- | :---: | :---: | :-------:
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[Ling et al. (2015)](http://www.cs.cmu.edu/~lingwang/papers/emnlp2015.pdf) | 97.78 | 94.03 | 96.18
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-[Andor et al. (2016)](http://arxiv.org/pdf/1603.06042v1.pdf)* | 97.77 | 94.80 | 96.86
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+[Andor et al. (2016)](http://arxiv.org/abs/1603.06042)* | 97.77 | 94.80 | 96.86
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Parsey McParseface | 97.52 | 94.24 | 96.45
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The first part of this tutorial describes how to install the necessary tools and
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@@ -475,7 +475,7 @@ predicts the next action to take.
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### Training a Parser Step 1: Local Pretraining
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-As described in our [paper](http://arxiv.org/pdf/1603.06042v1.pdf), the first
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+As described in our [paper](http://arxiv.org/abs/1603.06042), the first
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step in training the model is to *pre-train* using *local* decisions. In this
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phase, we use the gold dependency to guide the parser, and train a softmax layer
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to predict the correct action given these gold dependencies. This can be
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Gideon Wulfsohn