Neural network models for joint POS tagging and dependency parsing
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README.md

Neural Network Models for Joint POS Tagging and Dependency Parsing

Implementations of joint models for POS tagging and dependency parsing, as described in my papers:

  1. Dat Quoc Nguyen and Karin Verspoor. 2018. An improved neural network model for joint POS tagging and dependency parsing. In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 81-91. [.bib] (jPTDP v2.0)
  2. Dat Quoc Nguyen, Mark Dras and Mark Johnson. 2017. A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 134-142. [.bib] (jPTDP v1.0)

This github project currently supports jPTDP v2.0, while v1.0 can be found in the release section. Please cite paper [1] when jPTDP is used to produce published results or incorporated into other software. I would highly appreciate to have your bug reports, comments and suggestions about jPTDP. As a free open-source implementation, jPTDP is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

Installation

jPTDP requires the following software packages:

  • Python 2.7

  • DyNet v2.0

    $ virtualenv -p python2.7 .DyNet
    $ source .DyNet/bin/activate
    $ pip install cython numpy
    $ pip install dynet==2.0.3
    

Once you installed the prerequisite packages above, you can clone or download (and then unzip) jPTDP. Next sections show instructions to train a new joint model for POS tagging and dependency parsing, and then to utilize a pre-trained model.

Train a joint model

Suppose that SOURCE_DIR is simply used to denote the source code directory. Similar to files train.conllu and dev.conllu in folder SOURCE_DIR/sample or treebanks in the Universal Dependencies (UD) project, the training and development files are formatted following 10-column data format. For training, jPTDP will only use information from columns 1 (ID), 2 (FORM), 4 (Coarse-grained POS tags---UPOSTAG), 7 (HEAD) and 8 (DEPREL).

To train a joint model for POS tagging and dependency parsing, you perform:

SOURCE_DIR$ python jPTDP.py --dynet-seed 123456789 [--dynet-mem <int>] [--epochs <int>] [--lstmdims <int>] [--lstmlayers <int>] [--hidden <int>] [--wembedding <int>] [--cembedding <int>] [--pembedding <int>] [--prevectors <path-to-pre-trained-word-embedding-file>] [--model <String>] [--params <String>] --outdir <path-to-output-directory> --train <path-to-train-file>  --dev <path-to-dev-file>

where hyper-parameters in [] are optional:

  • --dynet-mem: Specify DyNet memory in MB.
  • --epochs: Specify number of training epochs. Default value is 30.
  • --lstmdims: Specify number of BiLSTM dimensions. Default value is 128.
  • --lstmlayers: Specify number of BiLSTM layers. Default value is 2.
  • --hidden: Specify size of MLP hidden layer. Default value is 100.
  • --wembedding: Specify size of word embeddings. Default value is 100.
  • --cembedding: Specify size of character embeddings. Default value is 50.
  • --pembedding: Specify size of POS tag embeddings. Default value is 100.
  • --prevectors: Specify path to the pre-trained word embedding file for initialization. Default value is "None" (i.e. word embeddings are randomly initialized).
  • --model: Specify a name for model parameters file. Default value is "model".
  • --params: Specify a name for model hyper-parameters file. Default value is "model.params".
  • --outdir: Specify path to directory where the trained model will be saved.
  • --train: Specify path to the training data file.
  • --dev: Specify path to the development data file.

For example:

SOURCE_DIR$ python jPTDP.py --dynet-seed 123456789 --dynet-mem 1000 --epochs 30 --lstmdims 128 --lstmlayers 2 --hidden 100 --wembedding 100 --cembedding 50 --pembedding 100  --model trialmodel --params trialmodel.params --outdir sample/ --train sample/train.conllu --dev sample/dev.conllu

will produce model files trialmodel and trialmodel.params in folder SOURCE_DIR/sample.

If you would like to use the fine-grained language-specific POS tags in the 5th column instead of the coarse-grained POS tags in the 4th column, you should use swapper.py in folder SOURCE_DIR/utils to swap contents in the 4th and 5th columns:

SOURCE_DIR$ python utils/swapper.py <path-to-train-(and-dev)-file>

For example:

SOURCE_DIR$ python utils/swapper.py sample/train.conllu
SOURCE_DIR$ python utils/swapper.py sample/dev.conllu

will generate two new files for training: train.conllu.ux2xu and dev.conllu.ux2xu in folder SOURCE_DIR/sample.

Utilize a pre-trained model

Assume that you are going to utilize a pre-trained model for annotating a corpus whose each line represents a tokenized/word-segmented sentence. You should use converter.py in folder SOURCE_DIR/utils to obtain a 10-column data format of this corpus:

SOURCE_DIR$ python utils/converter.py <file-path>

For example:

SOURCE_DIR$ python utils/converter.py sample/test

will generate in folder SOURCE_DIR/sample a file named test.conllu which can be used later as input to the pre-trained model.

To utilize a pre-trained model for POS tagging and dependency parsing, you perform:

SOURCE_DIR$ python jPTDP.py --predict --model <path-to-model-parameters-file> --params <path-to-model-hyper-parameters-file> --test <path-to-10-column-input-file> --outdir <path-to-output-directory> --output <String>
  • --model: Specify path to model parameters file.
  • --params: Specify path to model hyper-parameters file.
  • --test: Specify path to 10-column input file.
  • --outdir: Specify path to directory where output file will be saved.
  • --output: Specify name of the output file.

For example:

SOURCE_DIR$ python jPTDP.py --predict --model sample/trialmodel --params sample/trialmodel.params --test sample/test.conllu --outdir sample/ --output test.conllu.pred
SOURCE_DIR$ python jPTDP.py --predict --model sample/trialmodel --params sample/trialmodel.params --test sample/dev.conllu --outdir sample/ --output dev.conllu.pred

will produce output files test.conllu.pred and dev.conllu.pred in folder SOURCE_DIR/sample.

Pre-trained models

Pre-trained jPTDP v2.0 models, which were trained on English WSJ Penn treebank, GENIA and UD v2.2 treebanks, can be found at HERE. Results on test sets (as detailed in paper [1]) are as follows:

Treebank Model name POS UAS LAS
English WSJ Penn treebank model256 97.97 94.51 92.87
English WSJ Penn treebank model 97.88 94.25 92.58

model256 and model denote the pre-trained models which use 256- and 128-dimensional LSTM hidden states, respectively, i.e. model256 is more accurate but slower.

Treebank Code UPOS UAS LAS
UD_Afrikaans-AfriBooms af_afribooms 95.73 82.57 78.89
UD_Ancient_Greek-PROIEL grc_proiel 96.05 77.57 72.84
UD_Ancient_Greek-Perseus grc_perseus 88.95 65.09 58.35
UD_Arabic-PADT ar_padt 96.33 86.08 80.97
UD_Basque-BDT eu_bdt 93.62 79.86 75.07
UD_Bulgarian-BTB bg_btb 98.07 91.47 87.69
UD_Catalan-AnCora ca_ancora 98.46 90.78 88.40
UD_Chinese-GSD zh_gsd 93.26 82.50 77.51
UD_Croatian-SET hr_set 97.42 88.74 83.62
UD_Czech-CAC cs_cac 98.87 89.85 87.13
UD_Czech-FicTree cs_fictree 97.98 88.94 85.64
UD_Czech-PDT cs_pdt 98.74 89.64 87.04
UD_Czech-PUD cs_pud 96.71 87.62 82.28
UD_Danish-DDT da_ddt 96.18 82.17 78.88
UD_Dutch-Alpino nl_alpino 95.62 86.34 82.37
UD_Dutch-LassySmall nl_lassysmall 95.21 86.46 82.14
UD_English-EWT en_ewt 95.48 87.55 84.71
UD_English-GUM en_gum 94.10 84.88 80.45
UD_English-LinES en_lines 95.55 80.34 75.40
UD_English-PUD en_pud 95.25 87.49 84.25
UD_Estonian-EDT et_edt 96.87 85.45 82.13
UD_Finnish-FTB fi_ftb 94.53 86.10 82.45
UD_Finnish-PUD fi_pud 96.44 87.54 84.60
UD_Finnish-TDT fi_tdt 96.12 86.07 82.92
UD_French-GSD fr_gsd 97.11 89.45 86.43
UD_French-Sequoia fr_sequoia 97.92 89.71 87.43
UD_French-Spoken fr_spoken 94.25 79.80 73.45
UD_Galician-CTG gl_ctg 97.12 85.09 81.93
UD_Galician-TreeGal gl_treegal 93.66 77.71 71.63
UD_German-GSD de_gsd 94.07 81.45 76.68
UD_Gothic-PROIEL got_proiel 93.45 79.80 71.85
UD_Greek-GDT el_gdt 96.59 87.52 84.64
UD_Hebrew-HTB he_htb 96.24 87.65 82.64
UD_Hindi-HDTB hi_hdtb 96.94 93.25 89.83
UD_Hungarian-Szeged hu_szeged 92.07 76.18 69.75
UD_Indonesian-GSD id_gsd 93.29 84.64 77.71
UD_Irish-IDT ga_idt 89.74 75.72 65.78
UD_Italian-ISDT it_isdt 98.01 92.33 90.20
UD_Italian-PoSTWITA it_postwita 95.41 84.20 79.11
UD_Japanese-GSD ja_gsd 97.27 94.21 92.02
UD_Japanese-Modern ja_modern 70.53 66.88 49.51
UD_Korean-GSD ko_gsd 93.35 81.32 76.58
UD_Korean-Kaist ko_kaist 93.53 83.59 80.74
UD_Latin-ITTB la_ittb 98.12 82.99 79.96
UD_Latin-PROIEL la_proiel 95.54 74.95 69.76
UD_Latin-Perseus la_perseus 82.36 57.21 46.28
UD_Latvian-LVTB lv_lvtb 93.53 81.06 76.13
UD_North_Sami-Giella sme_giella 87.48 65.79 58.09
UD_Norwegian-Bokmaal no_bokmaal 97.73 89.83 87.57
UD_Norwegian-Nynorsk no_nynorsk 97.33 89.73 87.29
UD_Norwegian-NynorskLIA no_nynorsklia 85.22 64.14 54.31
UD_Old_Church_Slavonic-PROIEL cu_proiel 93.69 80.59 73.93
UD_Old_French-SRCMF fro_srcmf 95.12 86.65 81.15
UD_Persian-Seraji fa_seraji 96.66 88.07 84.07
UD_Polish-LFG pl_lfg 98.22 95.29 93.10
UD_Polish-SZ pl_sz 97.05 90.98 87.66
UD_Portuguese-Bosque pt_bosque 96.76 88.67 85.71
UD_Romanian-RRT ro_rrt 97.43 88.74 83.54
UD_Russian-SynTagRus ru_syntagrus 98.51 91.00 88.91
UD_Russian-Taiga ru_taiga 85.49 65.52 56.33
UD_Serbian-SET sr_set 97.40 89.32 85.03
UD_Slovak-SNK sk_snk 95.18 85.88 81.89
UD_Slovenian-SSJ sl_ssj 97.79 88.26 86.10
UD_Slovenian-SST sl_sst 89.50 66.14 58.13
UD_Spanish-AnCora es_ancora 98.57 90.30 87.98
UD_Swedish-LinES sv_lines 95.51 83.60 78.97
UD_Swedish-PUD sv_pud 92.10 79.53 74.53
UD_Swedish-Talbanken sv_talbanken 96.55 86.53 83.01
UD_Turkish-IMST tr_imst 92.93 70.53 62.55
UD_Ukrainian-IU uk_iu 95.24 83.47 79.38
UD_Urdu-UDTB ur_udtb 93.35 86.74 80.44
UD_Uyghur-UDT ug_udt 87.63 76.14 63.37
UD_Vietnamese-VTB vi_vtb 87.63 67.72 58.27