diaparser 1.0.0

Direct Attentive Dependency Parser

DiaParser

DiaParser provides a state-of-the-art direct attentive dependency parser based onthe Biaffine Parser (Dozat and Manning, 2017) architecture.

The parser can work directly on plain text or on tokenized text. The parser automatically dowloads pretrained models as well as tokenizers and produces dependency parsing trees, as detailed in Usage.

You can also train your own models and contribute them to the repository, to share with others.

Diaparser uses pretrained contextual embeddings for representing input from models in transformers.

Pretrained tokenizers are provided by Stanza.

Alternatively to contextual embeddings, Diaparser also allows to utilize CharLSTM layers to produce character/subword-level features. Both BERT and CharLSTM avoid the need of generating POS tags.

DiaParser is derived from SuPar, which provides additional variants of dependency and constituency parsers.

Contents

• Contents
• Installation
• Performance
• Usage
  • Training
  • Evaluation
• TODO
• References

Installation

DiaParser can be installed via pip:

$ pip install -U diaparser

Or installing from source is also permitted:

$ git clone https://github.com/Unipisa/diaparser && cd diaparser
$ python setup.py install

The package has the following requirements:

• python: >= 3.6
• pytorch: >= 1.4
• transformers: >= 3.1
• optional tokenizers stanza: >= 1.1.1

Performance

DiaParser provides pretrained models for English, Chinese and other 17 languages of the IWPT 2020 Shared task. English models are trained on the Penn Treebank (PTB) with Stanford Dependencies, with 39,832 training sentences, while Chinese models are trained on Penn Chinese Treebank version 7 (CTB7) with 46,572 training sentences. The other languages are trained on the Universal Dependencies treebanks v2.5.

The performance and parsing speed of these models are listed in the following table. Notably, punctuation is ignored in all evaluation metrics for PTB, but included in all the others.

Language	Corpus	Name	UAS	LAS	Speed (Sents/s)
English	PTB	en_ptb.electra	96.03	94.37	1826.77
Arabic	PADT	ar_padt.bert	87.75	83.25
Bulgarian	BTB	bg_btb.DeepPavlov	95.02	92.20
Czech	PDT	cs_pdt.DeepPavlov	94.02	92.06
English	EWT	en_ewt.electra	91.66	89.51
Estonian	EDT, EWT	et_edt_ewt.mbert	86.39	82.44
Finnish	TDT	fi_tdt.turkunlp	94.28	92.56
French	sequoia	fr_sequoia.camembert	92.81	89.55
Italian	ISDT	it_isdt.dbmdz	95.40	93.78
Latvian	LVBT	lv_lvtb.mbert	87.46	83.51
Lithuanian	ALKSNIS	lt_alksnis.mbert	80.09	75.14
Dutch	Alpino	nl_alpino.wietsedv	90.80	88.34
Polish	PDB, LFG	pl_pdb_lfg.dkleczek	94.38	91.70
Russian	SynTagRus	ru_syntagrus.DeepPavlov	94.97	93.72
Slovak	SNK	sk_snk.mbert	93.11	90.44
Swediskh	Talbanken	sv_talbanken.KB	90.79	88.08
Tamil	TTB	ta_ttb.mbert	74.20	66.49
Ukrainian	IU	uk_iu.TurkuNLP	90.39	87.61
Chinese	CTB	zh_ptb.hfl	92.14	85.74

These results were obtained on a server with Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz and Nvidia T4 GPU.

Usage

DiaParser is very easy to use. You can download a pretrained model and run syntactic parsing over sentences with a few lines of code:

>>> from diaparser import Parser
>>> parser = Parser.load('en_ewt-electra')
>>> dataset = parser.predict([['She', 'enjoys', 'playing', 'tennis', '.']], prob=True, verbose=False)
100%|####################################| 1/1 00:00<00:00, 85.15it/s

The call to parser.predict will return an instance of diaparser.utils.Dataset containing the predicted syntactic trees. You can either access any sentence within the dataset or an individual field of all the tokens.

>>> print(dataset.sentences[0])
1       She     _       _       _       _       2       nsubj   _       _
2       enjoys  _       _       _       _       0       root    _       _
3       playing _       _       _       _       2       xcomp   _       _
4       tennis  _       _       _       _       3       dobj    _       _
5       .       _       _       _       _       2       punct   _       _

>>> print(f"arcs:  {dataset.arcs[0]}\n"
          f"rels:  {dataset.rels[0]}\n"
          f"probs: {dataset.probs[0].gather(1,torch.tensor(dataset.arcs[0]).unsqueeze(1)).squeeze(-1)}")
arcs:  [2, 0, 2, 3, 2]
rels:  ['nsubj', 'root', 'xcomp', 'dobj', 'punct']
probs: tensor([1.0000, 0.9999, 0.9642, 0.9686, 0.9996])

Probabilities can be returned along with the results if prob=True.

If there are plenty of sentences to parse, DiaParser also supports loading them from file, and saving the results to a file specified with option pred.

>>> dataset = parser.predict('data/ptb/test.conllx', pred='pred.conllx')
2020-07-25 18:13:50 INFO Loading the data
2020-07-25 18:13:52 INFO
Dataset(n_sentences=2416, n_batches=13, n_buckets=8)
2020-07-25 18:13:52 INFO Making predictions on the dataset
100%|####################################| 13/13 00:01<00:00, 10.58it/s
2020-07-25 18:13:53 INFO Saving predicted results to pred.conllx
2020-07-25 18:13:54 INFO 0:00:01.335261s elapsed, 1809.38 Sents/s

Please make sure the file is in CoNLL-X or CoNLL-U format. If some fields are missing, you can use underscores as placeholders. An interface is provided for converting a list of tokens to a string in CoNLL-X format.

>>> from diaparser.utils import CoNLL
>>> print(CoNLL.toconll(['She', 'enjoys', 'playing', 'tennis', '.']))
1       She     _       _       _       _       _       _       _       _
2       enjoys  _       _       _       _       _       _       _       _
3       playing _       _       _       _       _       _       _       _
4       tennis  _       _       _       _       _       _       _       _
5       .       _       _       _       _       _       _       _       _

The CoNLL-U format for Universal Dependencies (UD) is also supported, with comments and extra annotations preserved and restored in the output.

>>> import os
>>> import tempfile
>>> text = '''# text = But I found the location wonderful and the neighbors very kind.
1\tBut\t_\t_\t_\t_\t_\t_\t_\t_
2\tI\t_\t_\t_\t_\t_\t_\t_\t_
3\tfound\t_\t_\t_\t_\t_\t_\t_\t_
4\tthe\t_\t_\t_\t_\t_\t_\t_\t_
5\tlocation\t_\t_\t_\t_\t_\t_\t_\t_
6\twonderful\t_\t_\t_\t_\t_\t_\t_\t_
7\tand\t_\t_\t_\t_\t_\t_\t_\t_
7.1\tfound\t_\t_\t_\t_\t_\t_\t_\t_
8\tthe\t_\t_\t_\t_\t_\t_\t_\t_
9\tneighbors\t_\t_\t_\t_\t_\t_\t_\t_
10\tvery\t_\t_\t_\t_\t_\t_\t_\t_
11\tkind\t_\t_\t_\t_\t_\t_\t_\t_
12\t.\t_\t_\t_\t_\t_\t_\t_\t_

'''
>>> path = os.path.join(tempfile.mkdtemp(), 'data.conllx')
>>> with open(path, 'w') as f:
...     f.write(text)
...
>>> print(parser.predict(path, verbose=False).sentences[0])
100%|####################################| 1/1 00:00<00:00, 68.60it/s
# text = But I found the location wonderful and the neighbors very kind.
1       But     _       _       _       _       3       cc      _       _
2       I       _       _       _       _       3       nsubj   _       _
3       found   _       _       _       _       0       root    _       _
4       the     _       _       _       _       5       det     _       _
5       location        _       _       _       _       6       nsubj   _       _
6       wonderful       _       _       _       _       3       xcomp   _       _
7       and     _       _       _       _       6       cc      _       _
7.1     found   _       _       _       _       _       _       _       _
8       the     _       _       _       _       9       det     _       _
9       neighbors       _       _       _       _       11      dep     _       _
10      very    _       _       _       _       11      advmod  _       _
11      kind    _       _       _       _       6       conj    _       _
12      .       _       _       _       _       3       punct   _       _

Training

To train a model from scratch, it is preferred to use the command-line option, which is more flexible and customizable. Here are some training examples:

# Biaffine Dependency Parser
# some common and default arguments are stored in config.ini
$ python -m diaparser.cmds.biaffine_dependency train -b -d 0  \
    -c config.ini  \
    -p exp/en_ptb.char/model  \
    -f char
# to use BERT, `-f` and `--bert` (default to bert-base-cased) should be specified
$ python -m diaparser.cmds.biaffine_dependency train -b -d 0  \
    -p exp/en_ptb.bert-base/model  \
    -f bert  \
    --bert bert-base-cased

For further instructions on training, please type python -m diaparser.cmds.<parser> train -h.

Alternatively, DiaParser provides an equivalent command entry points registered in setup.py: diaparser.

$ diaparser train -b -d 0 -c config.ini -p exp/en_ptb.electra-base/model -f bert --bert google/electra-base-discriminator

For handling large models, distributed training is also supported:

$ python -m torch.distributed.launch --nproc_per_node=4 --master_port=10000  \
    -m parser.cmds.biaffine_dependency train -b -d 0,1,2,3  \
    -p exp/en_ptb.electra-base/model  \
    -f bert --bert google/electra-base-discriminator

You may consult the PyTorch documentation and tutorials for more details.

Evaluation

The evaluation process resembles prediction:

>>> parser = Parser.load('biaffine-dep-en')
>>> loss, metric = parser.evaluate('data/ptb/test.conllx')
2020-07-25 20:59:17 INFO Loading the data
2020-07-25 20:59:19 INFO
Dataset(n_sentences=2416, n_batches=11, n_buckets=8)
2020-07-25 20:59:19 INFO Evaluating the dataset
2020-07-25 20:59:20 INFO loss: 0.2326 - UCM: 61.34% LCM: 50.21% UAS: 96.03% LAS: 94.37%
2020-07-25 20:59:20 INFO 0:00:01.253601s elapsed, 1927.25 Sents/s

TODO

• Provide a repository where to upload models, like HuggingFace.

References

• Timothy Dozat and Christopher D. Manning. 2017. Deep Biaffine Attention for Neural Dependency Parsing.
• Tao Ji, Yuanbin Wu and Man Lan. 2019. Graph-based Dependency Parsing with Graph Neural Networks.
• Terry Koo, Amir Globerson, Xavier Carreras and Michael Collins. 2007. Structured Prediction Models via the Matrix-Tree Theorem.
• Xuezhe Ma and Eduard Hovy. 2017. Neural Probabilistic Model for Non-projective MST Parsing.
• Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig and Eduard Hovy. 2018. Stack-Pointer Networks for Dependency Parsing.
• Xinyu Wang, Jingxian Huang, and Kewei Tu. 2019. Second-Order Semantic Dependency Parsing with End-to-End Neural Networks.
• Yu Zhang, Houquan Zhou and Zhenghua Li. 2020. Fast and Accurate Neural CRF Constituency Parsing.
• Yu Zhang, Zhenghua Li and Min Zhang. 2020. Efficient Second-Order TreeCRF for Neural Dependency Parsing.