Description:

TUPA 1.4.2

TUPA is a transition-based parser for Universal Conceptual Cognitive
Annotation (UCCA).

Requirements

Python 3.6



Install
Create a Python virtual environment. For example, on Linux:
virtualenv --python=/usr/bin/python3 venv
. venv/bin/activate # on bash
source venv/bin/activate.csh # on csh
Install the latest release:
pip install tupa
Alternatively, install the latest code from GitHub (may be unstable):
git clone https://github.com/danielhers/tupa
cd tupa
pip install .

Train the parser
Having a directory with UCCA passage files (for example, the English
Wiki
corpus),
run:
python -m tupa -t <train_dir> -d <dev_dir> -c <model_type> -m <model_filename>
The possible model types are sparse, mlp, and bilstm.



Parse a text file
Run the parser on a text file (here named example.txt) using a
trained model:
python -m tupa example.txt -m <model_filename>
An xml file will be created per passage (separate by blank lines in
the text file).


Pre-trained models
To download and extract a model pre-trained on the Wiki
corpus,
run:
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10.tar.gz
tar xvzf ucca-bilstm-1.3.10.tar.gz
Run the parser using the model:
python -m tupa example.txt -m models/ucca-bilstm


Other languages
To get a
model
pre-trained on the French *20K Leagues*
corpus
or a
model
pre-trained on the German *20K Leagues*
corpus,
run:
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-fr.tar.gz
tar xvzf ucca-bilstm-1.3.10-fr.tar.gz
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.3.10/ucca-bilstm-1.3.10-de.tar.gz
tar xvzf ucca-bilstm-1.3.10-de.tar.gz
Run the parser on a French/German text file (separate passages by blank
lines):
python -m tupa exemple.txt -m models/ucca-bilstm-fr --lang fr
python -m tupa beispiel.txt -m models/ucca-bilstm-de --lang de

Using BERT
BERT can be used instead of standard word embeddings. First, install the
required dependencies:
pip install -r requirements.bert.txt
Then pass the --use-bert argument to the training command.
See the possible configuration options in config.py (relevant
options have the prefix bert).



BERT Multilingual Training
A multilingual model can be trained, to leverage cross-lingual transfer
and improve results on low-resource languages:

Make sure the input passage files have the lang attribute. See
the script
`set_lang <https://github.com/huji-nlp/semstr/blob/master/semstr/scripts/set_lang.py>`__
in the package semstr.
Enable BERT by passing the --use-bert argument.
Use the multilingual model by passing
--bert-model=bert-base-multilingual-cased.
Pass the --bert-multilingual=0 argument to enable multilingual
training.



BERT Performance
Here are the average results over 3 BERT multilingual models trained on
the German *20K Leagues*
corpus,
English Wiki
corpus
and only on 15 sentences from the French *20K Leagues*
corpus,
with the following settings:
bert-model=bert-base-multilingual-cased
bert-layers=-1 -2 -3 -4
bert-layers-pooling=weighted
bert-token-align-by=sum
The results:


description
test primary F1
test remote F1
test average



German 20K Leagues
0.828
0.6723
0.824

English 20K Leagues
0.763
0.359
0.755

French 20K Leagues
0.739
0.46
0.732

English Wiki
0.789
0.581
0.784



*English *20K Leagues*
corpus
is used as out of domain test.


Pre-trained Models with BERT
To download and extract a multilingual
model
trained with the settings above, run:
curl -LO https://github.com/huji-nlp/tupa/releases/download/v1.4.0/bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz
tar xvzf bert_multilingual_layers_4_layers_pooling_weighted_align_sum.tar.gz
To run the parser using the model, use the following command. Pay
attention that you need to replace [lang] with the right language
symbol (fr, en, or de):
python -m tupa example.txt --lang [lang] -m bert_multilingual_layers_4_layers_pooling_weighted_align_sum

Author

Daniel Hershcovich: daniel.hershcovich@gmail.com



Contributors

Ofir Arviv: ofir.arviv@mail.huji.ac.il



Citation
If you make use of this software, please cite the following
paper:
@InProceedings{hershcovich2017a,
author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
title = {A Transition-Based Directed Acyclic Graph Parser for {UCCA}},
booktitle = {Proc. of ACL},
year = {2017},
pages = {1127--1138},
url = {http://aclweb.org/anthology/P17-1104}
}
The version of the parser used in the paper is
v1.0. To
reproduce the experiments, run:
curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2017.sh | bash
If you use the French, German or multitask models, please cite the
following paper:
@InProceedings{hershcovich2018multitask,
author = {Hershcovich, Daniel and Abend, Omri and Rappoport, Ari},
title = {Multitask Parsing Across Semantic Representations},
booktitle = {Proc. of ACL},
year = {2018},
pages = {373--385},
url = {http://aclweb.org/anthology/P18-1035}
}
The version of the parser used in the paper is
v1.3.3. To
reproduce the experiments, run:
curl -L https://raw.githubusercontent.com/huji-nlp/tupa/master/experiments/acl2018.sh | bash


License
This package is licensed under the GPLv3 or later license (see
`LICENSE.txt <LICENSE.txt>`__).

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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