# CoNLL2017 Shared Task Instructions

We are pleased to provide a competitive baseline for the [CoNLL2017 Shared Task
on Dependency Parsing](http://universaldependencies.org/conll17/). Note that we
are providing detailed tutorials to make it easier to use DRAGNN as a platform
for improving upon the baselines.

Please see our [paper](paper.pdf) more technical details about the model.

## Running the baselines

*   Install SyntaxNet/DRAGNN following the install instructions.
*   Download the models [here](https://drive.google.com/file/d/0BxpbZGYVZsEeSFdrUnBNMUp1YzQ/view?usp=sharing)
*   Download the contest [data and tools](http://universaldependencies.org/conll17/)
*   Run the baseline_eval.py to run the pre-trained tokenizer and evaluate on
    the dev set.

You should obtain the following results on the dev sets with gold
segmentation. Note: Our segmenter does not split multi-word tokens, which may
not play nice (yet) the official evaluation script.

| Language | UAS | LAS |
| -------- | :--------: | :-------------: |
| Ancient_Greek-PROIEL | 81.52	| 76.87 |
| Ancient_Greek	| 70.96	| 65.13 |
| Arabic	| 84.79	| 78.90 |
| Basque	| 80.96	| 77.19 |
| Bulgarian	| 91.33	| 86.77 |
| Catalan	| 91.32	| 88.76 |
| Chinese	| 77.56	| 71.96 |
| Croatian	| 86.62	| 81.84 |
| Czech-CAC	| 89.99	| 86.09 |
| Czech-CLTT	| 78.25	| 73.70 |
| Czech	| 89.55	| 85.23 |
| Danish	| 84.69	| 81.36 |
| Dutch-LassySmall | 84.12	| 80.85 |
| Dutch	| 86.68	| 81.91 |
| English-LinES	| 82.43	| 78.46 |
| English-ParTUT	| 83.55	| 79.00 |
| English	| 87.60	| 84.20 |
| Estonian	| 75.77	| 67.76 |
| Finnish-FTB	| 87.54	| 83.70 |
| Finnish	| 87.05	| 83.33 |
| French-ParTUT	| 85.12	| 80.79 |
| French-Sequoia	| 87.90	| 85.74 |
| French	| 91.05	| 88.48 |
| Galician-TreeGal | 75.26	| 69.50 |
| Galician	| 84.64	| 81.58 |
| German	| 85.53	| 81.27 |
| Gothic	| 81.79	| 74.99 |
| Greek	| 86.99	| 84.23 |
| Hebrew	| 87.79	| 82.18 |
| Hindi	| 93.73	| 90.10 |
| Hungarian	| 78.68	| 73.03 |
| Indonesian	| 83.02	| 76.51 |
| Irish	| 75.02	| 65.66 |
| Italian-ParTUT	| 85.09	| 80.90 |
| Italian	| 90.73	| 87.71 |
| Japanese	| 95.33	| 93.99 |
| Kazakh	| 28.09	| 7.87 |
| Korean	| 81.21	| 76.78 |
| Latin-ITTB	| 82.86	| 78.43 |
| Latin-PROIEL	| 79.52	| 73.58 |
| Latin	| 64.72	| 54.59 |
| Latvian	| 76.17	| 70.55 |
| Norwegian-Bokmaal | 91.23	| 88.79 |
| Norwegian-Nynorsk | 89.32	| 86.67 |
| Old_Church_Slavonic | 84.96	| 79.65 |
| Persian	| 87.70	| 83.98 |
| Polish	| 91.32	| 86.83 |
| Portuguese-BR	| 92.36	| 90.60 |
| Portuguese	| 90.60	| 88.12 |
| Romanian	| 89.41	| 83.00 |
| Russian-SynTagRus | 91.51	| 89.05 |
| Russian	| 85.18	| 80.71 |
| Slovak	| 88.08	| 82.64 |
| Slovenian-SST	| 66.77	| 59.38 |
| Slovenian	| 89.85	| 87.62 |
| Spanish-AnCora | 91.02	| 88.61 |
| Spanish	| 90.32	| 87.16 |
| Swedish-LinES	| 83.67	| 78.96 |
| Swedish	| 82.45	| 78.75 |
| Turkish	| 68.81	| 60.57 |
| Ukrainian	| 72.19	| 62.79 |
| Urdu	| 85.50	| 79.19 |
| Uyghur	| 69.23	| 43.27 |
| Vietnamese	| 65.18	| 55.61 |

## Using DRAGNN for developing your own models

We hope that DRAGNN will be useful as a starting point for deep learning parsing
methods. We've provided a few recipes for alternative baselines sprinkled
through the tutorials and examples.