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This repository uses the CONLL-U evaluation script available at https://github.com/ufal/conll2017 to make a comparison regarding accuracy between UDPipe models and Spacy models which are trained on the same treebanks. In order to do the comparison, the evaluation script used in the CoNLL 2017 Shared Task explained at http://universaldependencies.org/conll17/evaluation.html is used.

Below the output is reported from the CONLL17 evaluation scripts for the udpipe and spacy models. The most used results are the one from AligndAcc which indicate Gold accuracies which means that if we know the tokenisation, how good would the parts-of-speech tagging, morphological feature tagging and dependency parsing be.

Overall comparison graphs

The following shows the graphs comparing the UDPipe and spaCy models using the evaluation script used in the CoNLL 2017 Shared Task. It shows the word-aligned accuracies of the different NLP tasks and also the F1 measure.

Aligned Accuracies

F1 measure

Conclusion

You can look at the numbers below but when looking at the metrics below AligndAcc, they seem to give the following conclusion:

French Sequioa

Evaluation data from https://github.com/UniversalDependencies/UD_French-Sequoia release 2.0-test

Notes: This treebank does not contain xpos so measures of XPOS are irrelevant.

udpipe

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_udpipe.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.84 |     99.85 |     99.84 |
Sentences  |     92.00 |     95.83 |     93.88 |
Words      |     98.90 |     99.36 |     99.13 |
UPOS       |     95.81 |     96.26 |     96.03 |     96.88
XPOS       |     98.90 |     99.36 |     99.13 |    100.00
Feats      |     94.95 |     95.39 |     95.17 |     96.00
AllTags    |     93.88 |     94.32 |     94.10 |     94.92
Lemmas     |     96.62 |     97.07 |     96.85 |     97.70
UAS        |     83.77 |     84.16 |     83.96 |     84.70
LAS        |     81.19 |     81.57 |     81.38 |     82.09
CLAS       |     77.25 |     76.82 |     77.03 |     76.98

spacy

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_spacy.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     97.53 |     98.80 |     98.16 |
Sentences  |     78.49 |     88.82 |     83.33 |
Words      |     94.41 |     92.69 |     93.54 |
UPOS       |     90.84 |     89.18 |     90.00 |     96.22
XPOS       |     94.41 |     92.69 |     93.54 |    100.00
Feats      |     89.94 |     88.30 |     89.11 |     95.27
AllTags    |     88.87 |     87.25 |     88.06 |     94.14
Lemmas     |     80.21 |     78.75 |     79.47 |     84.96
UAS        |     77.38 |     75.97 |     76.67 |     81.96
LAS        |     74.12 |     72.77 |     73.43 |     78.51
CLAS       |     71.35 |     71.76 |     71.56 |     73.65

Dutch

Evaluation data from https://github.com/UniversalDependencies/UD_Dutch release 2.0-test

Notes: None

udpipe

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_udpipe.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.85 |     99.83 |     99.84 |
Sentences  |     95.30 |     97.66 |     96.47 |
Words      |     99.85 |     99.83 |     99.84 |
UPOS       |     91.74 |     91.71 |     91.72 |     91.87
XPOS       |     88.71 |     88.68 |     88.69 |     88.84
Feats      |     89.82 |     89.80 |     89.81 |     89.95
AllTags    |     87.59 |     87.57 |     87.58 |     87.72
Lemmas     |     90.01 |     89.99 |     90.00 |     90.14
UAS        |     76.79 |     76.77 |     76.78 |     76.91
LAS        |     70.95 |     70.93 |     70.94 |     71.05
CLAS       |     63.92 |     63.11 |     63.51 |     63.22

spacy

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_spacy.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     97.30 |     99.02 |     98.15 |
Sentences  |     84.91 |     91.97 |     88.30 |
Words      |     97.30 |     99.02 |     98.15 |
UPOS       |     76.81 |     78.16 |     77.48 |     78.94
XPOS       |     86.71 |     88.24 |     87.47 |     89.11
Feats      |     87.82 |     89.36 |     88.58 |     90.25
AllTags    |     73.29 |     74.58 |     73.93 |     75.32
Lemmas     |     69.16 |     70.38 |     69.77 |     71.08
UAS        |     76.51 |     77.85 |     77.17 |     78.62
LAS        |     70.35 |     71.59 |     70.97 |     72.30
CLAS       |     63.02 |     64.52 |     63.76 |     65.57

Spanish-Ancora

Evaluation data from https://github.com/UniversalDependencies/UD_Spanish-Ancora release 2.0-test

Notes: None

udpipe

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_udpipe.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.96 |     99.96 |     99.96 |
Sentences  |     98.62 |     99.30 |     98.96 |
Words      |     99.95 |     99.94 |     99.94 |
UPOS       |     98.10 |     98.10 |     98.10 |     98.16
XPOS       |     98.10 |     98.10 |     98.10 |     98.16
Feats      |     97.49 |     97.48 |     97.49 |     97.54
AllTags    |     96.84 |     96.83 |     96.83 |     96.89
Lemmas     |     98.09 |     98.08 |     98.08 |     98.14
UAS        |     87.72 |     87.71 |     87.72 |     87.76
LAS        |     84.60 |     84.59 |     84.59 |     84.64
CLAS       |     78.93 |     78.75 |     78.84 |     78.84

spacy

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_spacy.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.23 |     99.69 |     99.46 |
Sentences  |     98.44 |     99.24 |     98.84 |
Words      |     98.88 |     98.99 |     98.93 |
UPOS       |     94.23 |     94.34 |     94.28 |     95.30
XPOS       |     96.96 |     97.08 |     97.02 |     98.06
Feats      |     96.53 |     96.64 |     96.58 |     97.62
AllTags    |     93.02 |     93.12 |     93.07 |     94.07
Lemmas     |     80.20 |     80.29 |     80.24 |     81.11
UAS        |     86.67 |     86.77 |     86.72 |     87.66
LAS        |     83.96 |     84.06 |     84.01 |     84.92
CLAS       |     78.85 |     78.56 |     78.70 |     80.06

Portuguese

Evaluation data from https://github.com/UniversalDependencies/UD_Portuguese release 2.0-test

Notes: spacy does not return morphological features resulting in incorrect evaluation numbers for spacy on Feats and AllTags

udpipe

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_udpipe.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.69 |     99.77 |     99.73 |
Sentences  |     95.50 |     97.90 |     96.69 |
Words      |     99.52 |     99.69 |     99.60 |
UPOS       |     96.35 |     96.51 |     96.43 |     96.81
XPOS       |     72.73 |     72.86 |     72.79 |     73.08
Feats      |     93.35 |     93.51 |     93.43 |     93.80
AllTags    |     71.64 |     71.76 |     71.70 |     71.98
Lemmas     |     96.79 |     96.95 |     96.87 |     97.26
UAS        |     86.58 |     86.73 |     86.65 |     87.00
LAS        |     83.04 |     83.18 |     83.11 |     83.44
CLAS       |     77.27 |     76.70 |     76.98 |     77.06

spacy

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_spacy.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     95.32 |     98.10 |     96.69 |
Sentences  |     87.50 |     93.92 |     90.60 |
Words      |     90.32 |     86.21 |     88.22 |
UPOS       |     82.41 |     78.65 |     80.48 |     91.23
XPOS       |     60.34 |     57.59 |     58.94 |     66.81
Feats      |     30.47 |     29.09 |     29.76 |     33.74
AllTags    |     24.23 |     23.13 |     23.66 |     26.83
Lemmas     |     74.53 |     71.13 |     72.79 |     82.51
UAS        |     72.49 |     69.19 |     70.80 |     80.26
LAS        |     68.08 |     64.97 |     66.49 |     75.37
CLAS       |     65.28 |     68.14 |     66.68 |     69.30

Italian

Evaluation data from https://github.com/UniversalDependencies/UD_Italian release 2.0-test

Notes: None

udpipe

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_udpipe.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.91 |     99.92 |     99.91 |
Sentences  |     96.73 |     98.34 |     97.53 |
Words      |     99.82 |     99.85 |     99.83 |
UPOS       |     97.21 |     97.24 |     97.22 |     97.38
XPOS       |     97.01 |     97.03 |     97.02 |     97.18
Feats      |     96.99 |     97.01 |     97.00 |     97.16
AllTags    |     96.10 |     96.13 |     96.12 |     96.28
Lemmas     |     97.28 |     97.31 |     97.30 |     97.46
UAS        |     88.90 |     88.92 |     88.91 |     89.06
LAS        |     86.20 |     86.22 |     86.21 |     86.36
CLAS       |     79.81 |     79.49 |     79.65 |     79.67

spacy

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_spacy.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     97.10 |     94.66 |     95.86 |
Sentences  |     95.74 |     97.93 |     96.82 |
Words      |     90.39 |     81.89 |     85.93 |
UPOS       |     82.75 |     74.96 |     78.66 |     91.55
XPOS       |     86.39 |     78.27 |     82.13 |     95.58
Feats      |     86.96 |     78.78 |     82.66 |     96.20
AllTags    |     81.78 |     74.09 |     77.75 |     90.48
Lemmas     |     71.73 |     64.98 |     68.19 |     79.36
UAS        |     70.98 |     64.30 |     67.47 |     78.52
LAS        |     67.31 |     60.98 |     63.99 |     74.47
CLAS       |     59.85 |     61.85 |     60.84 |     66.76

English (mark udpipe trained on UD_English, spacy was trained on Ontonotes)

Evaluation data from https://github.com/UniversalDependencies/UD_English release 2.1

Notes:

udpipe

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_udpipe.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     99.15 |     98.94 |     99.05 |
Sentences  |     93.49 |     96.82 |     95.13 |
Words      |     99.15 |     98.94 |     99.05 |
UPOS       |     93.77 |     93.58 |     93.68 |     94.58
XPOS       |     93.15 |     92.95 |     93.05 |     93.95
Feats      |     94.59 |     94.40 |     94.50 |     95.41
AllTags    |     91.81 |     91.61 |     91.71 |     92.59
Lemmas     |     96.16 |     95.96 |     96.06 |     96.99
UAS        |     83.09 |     82.91 |     83.00 |     83.80
LAS        |     79.97 |     79.80 |     79.88 |     80.65
CLAS       |     76.20 |     75.95 |     76.07 |     76.81

spacy

> system("python evaluation_script/conll17_ud_eval.py -v gold.conllu predictions_spacy.conllu")
Metrics    | Precision |    Recall |  F1 Score | AligndAcc
-----------+-----------+-----------+-----------+-----------
Tokens     |     96.71 |     98.26 |     97.48 |
Sentences  |     87.70 |     94.46 |     90.96 |
Words      |     96.71 |     98.26 |     97.48 |
UPOS       |     79.93 |     81.21 |     80.56 |     82.65
XPOS       |     89.60 |     91.04 |     90.31 |     92.65
Feats      |     32.51 |     33.03 |     32.77 |     33.62
AllTags    |     27.65 |     28.09 |     27.87 |     28.59
Lemmas     |     85.79 |     87.17 |     86.48 |     88.72
UAS        |     56.00 |     56.90 |     56.44 |     57.90
LAS        |     42.46 |     43.15 |     42.80 |     43.91
CLAS       |     36.69 |     43.30 |     39.72 |     44.14

German

Not executed as the spacy model is built on a different treebank, which will give similar remarks as encountered in the English evaluation.