The tagging performance of DRAGNN is worse than SyntaxNet.

[banyh]

System information

• the top-level directory of the model: /home/banyhong/syntaxnet_wrapper/syntaxnet_wrapper/models/syntaxnet
• OS Platform: Linux Ubuntu 14.04
• TensorFlow binary: Linux CPU version, 1.1.0-rc1
• TensorFlow version: ('v1.1.0-rc1-168-g0054c39', '1.1.0-rc1')
• Bazel version: 0.4.3

Describe the problem

The tagging performance of DRAGNN is worse than SyntaxNet.

I've modified dragnn/tools/parse-to-conll.py to make it printint out token.tag.

The sentence "Alice drove down the street in her car" has been parsed by SyntaxNet tagger+parser, DRAGNN parser. The pos-tag of Alice is NOUN++NNP in SyntaxNet, but is ADV++RB in DRAGNN.

Source code / logs

Modified parse-to-conll.py line 227 to 231:

            f.write('%s\t%s\t_\t_\t_\t_\t%d\t%s\t_\t%s\n'%(
                i + 1,
                token.word.encode('utf-8'), head,
                token.label.encode('utf-8'),
                token.tag.encode('utf-8')))

To get DRAGNN parser result:

bazel --output_user_root=bazel_root run -c opt //dragnn/tools:parse-to-conll -- \
    --parser_master_spec=/home/banyhong/syntaxnet_wrapper/syntaxnet_wrapper/models/syntaxnet/dragnn/conll17/English/parser_spec.textproto \
    --parser_checkpoint_file=/home/banyhong/syntaxnet_wrapper/syntaxnet_wrapper/models/syntaxnet/dragnn/conll17/English/checkpoint \
    --parser_resource_dir=/home/banyhong/syntaxnet_wrapper/syntaxnet_wrapper/models/syntaxnet/dragnn/conll17/English \
    --use_gold_segmentation=True \
    --input_file=/home/banyhong/syntaxnet_wrapper/syntaxnet_wrapper/models/syntaxnet/input.conll \
    --inference_beam_size=char_lstm=16,lookahead=16,tagger=64,parser=64 \
    --output_file=/home/banyhong/syntaxnet_wrapper/syntaxnet_wrapper/models/syntaxnet/output.conll

The content in output.conll is:

#Alice drove down the street in her car
1       Alice   _       _       _       _       2       nsubj   _       attribute { name: "fPOS" value: "ADV++RB" }
2       drove   _       _       _       _       0       root    _       attribute { name: "Mood" value: "Imp" } attribute { name: "VerbForm" value: "Fin" } attribute { name: "fPOS" value: "VERB++VB" }
3       down    _       _       _       _       2       compound:prt    _       attribute { name: "fPOS" value: "ADP++RP" }
4       the     _       _       _       _       5       det     _       attribute { name: "Definite" value: "Def" } attribute { name: "PronType" value: "Art" } attribute { name: "fPOS" value: "DET++DT" }
5       street  _       _       _       _       2       obj     _       attribute { name: "Number" value: "Sing" } attribute { name: "fPOS" value: "NOUN++NN" }
6       in      _       _       _       _       8       case    _       attribute { name: "fPOS" value: "ADP++IN" }
7       her     _       _       _       _       8       nmod:poss       _       attribute { name: "Gender" value: "Fem" } attribute { name: "Number" value: "Sing" } attribute { name: "Person" value: "3" } attribute { name: "Poss" value: "Yes" } attribute { name: "PronType" value: "Prs" } attribute { name: "fPOS" value: "PRON++PRP$" }
8       car     _       _       _       _       5       nmod    _       attribute { name: "Number" value: "Sing" } attribute { name: "fPOS" value: "NOUN++NN" }

The parsing result of SyntaxNet is:

1       Alice   _       NOUN    NNP     _       2       nsubj   _       _
2       drove   _       VERB    VBD     _       0       ROOT    _       _
3       down    _       ADP     IN      _       2       prep    _       _
4       the     _       DET     DT      _       5       det     _       _
5       street  _       NOUN    NN      _       3       pobj    _       _
6       in      _       ADP     IN      _       2       prep    _       _
7       her     _       PRON    PRP$    _       8       poss    _       _
8       car     _       NOUN    NN      _       6       pobj    _       _

[bogatyy]

Could you clarify the language and the dataset that you evaluated POS tagging on?
Or do you mean tagging is worse on that specific sentence?

[banyh]

Here is the information:

• Language: English
• SyntaxNet model: parsey_mcparseface
• DragNN model: English
• Tag system: Penn Tree Bank POS tags
• Dataset: ud-treebanks-conll2017.tgz
  • using file ud-treebanks-conll2017/UD_English/en-ud-dev.conllu
  • 2002 sentences in total
  • 25148 tags in total
• SyntaxNet accuracy: 0.9303
• DragNN accuracy: 0.9217

[bogatyy]

Oh, that is because parsey_mcparseface was trained on a significantly larger dataset. Further, it was optimized to maximize both POS tagging accuracy and parsing accuracy.
On the other hand, our baseline models for CoNLL 2017 (including the English model you looked at) could only be trained on UD data and were optimized only for parsing accuracy.
To learn more: http://universaldependencies.org/conll17/evaluation.html