Transition-based UCCA Parser
Project description
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
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>`__).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file TUPA-1.4.2.tar.gz
.
File metadata
- Download URL: TUPA-1.4.2.tar.gz
- Upload date:
- Size: 2.3 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
1321df4a79e5ba751302cf45a46da6de2b2984a22d228622b22c8f56cb17a709
|
|
MD5 |
85e083fd0e1f88e92cafc17dcb3f2560
|
|
BLAKE2b-256 |
bb0a1eac685f12c287d734a552486ee7e38f1e83aa005c05569911d8865c3349
|
File details
Details for the file TUPA-1.4.2-py3-none-any.whl
.
File metadata
- Download URL: TUPA-1.4.2-py3-none-any.whl
- Upload date:
- Size: 111.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.7
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 |
f49ac79838b6f45485943437831fac3d5cd40d26204793b4a162ef48cbdc3816
|
|
MD5 |
96015c3391404bba79d4b14f1d4974a5
|
|
BLAKE2b-256 |
3ddaf39709860dc5ad7f55e5020091f92446ba981151ff88ce95ad46388191ae
|