This is the repository for the paper Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach, presented at COLING 2020 by Simone Conia and Roberto Navigli.
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between high-resource (e.g., English) and low-resource (e.g., German) settings. To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages. Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available. Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling.
If you use any part of this work, please consider citing the paper as follows:
@inproceedings{conia-navigli-2020-multilingual-srl,
title = "Bridging the Gap in Multilingual {S}emantic {R}ole {L}abeling: {A} Language-Agnostic Approach",
author = "Conia, Simone and Navigli, Roberto",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.120",
doi = "10.18653/v1/2020.coling-main.120",
pages = "1396--1410",
}
Here we provide an overview of the results obtained by our model on CoNLL-2009 (English, Czech, German, Catalan, Spanish, Chinese) and CoNLL-2012.
Results on the CoNLL-2009 Shared Task by Hajic et al., 2009. The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages.
Results on the CoNLL-2012 Shared Task by Pradhan et al., 2012. CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes.
You'll need a working Python environment to run the code. The recommended way to set up your environment is through the Anaconda Python distribution which provides the conda package manager. Anaconda can be installed in your user directory and does not interfere with the system Python installation.
We use conda virtual environments to manage the project dependencies in
isolation. Thus, you can install our dependencies without causing conflicts with your
setup (even with different Python versions).
Run the following command and follow the steps to create a separate environment:
> bash setup.sh
> Enter environment name (recommended: multilingual-srl): multilingual-srl
> Enter python version (recommended: 3.8): 3.8
> Enter cuda version (e.g. '11.3' or 'none' to avoid installing cuda support):All the code in this repository was tested using Python 3.8 and CUDA 11.3.
NOTE: If you don't want to retrain or evaluate a model, you can skip this step.
Depending on the task you want to perform (e.g., dependency-based SRL or span-based SRL), you need to obtain some datasets (unfortunately, some of these datasets require a license fee).
NOTE: Not all of the following datasets are required. E.g., if you are only interested in dependency-based SRL with PropBank labels, you just need CoNLL-2009.
- Hajic et al., 2009. The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages. The dataset is available on LDC (LDC2012T04).
- Pradhan et al., 2012. CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes. The dataset is available on LDC (LDC2013T19).
- OntoNotes 5 tagged with PropBank 3.x.
- UniteD-SRL v2 (Coming soon!).
Once you have downloaded and unzipped the data, place it in data/original as follows:
data/original/
├── conll2009
│ ├── ca
│ │ ├── CoNLL2009_dev.txt
│ │ ├── CoNLL2009_test.txt
│ │ └── CoNLL2009_train.txt
│ ├── cs
│ │ ├── CoNLL2009_dev.txt
│ │ ├── CoNLL2009_test-ood.txt
│ │ ├── CoNLL2009_test.txt
│ │ └── CoNLL2009_train.txt
│ ├── de
│ │ ├── CoNLL2009_dev.txt
...
├── conll2012
│ └── en
│ ├── CoNLL2012_dev.txt
│ ├── CoNLL2012_test.txt
│ └── CoNLL2012_train.txt
├── ontonotes
│ ├── ontonotes_dev.txt
│ ├── ontonotes_test-conll2012.txt
│ ├── ontonotes_test.txt
│ └── ontonotes_train.txt
...
└── united
├── EN
│ ├── dependency
│ │ ├── test.conllu
│ │ ├── train.conllu
│ │ ├── train.extra.conllu
│ │ └── validation.conllu
│ └── span
│ ├── test.conllu
│ ├── train.conllu
│ ├── train.extra.conllu
│ └── validation.conllu
├── ES
│ ├── dependency
│ │ ├── test.conllu
│ │ ├── train.extra.conllu
│ │ └── validation.conllu
...
Note: Make sure that the datasets are renamed as specified in the example above.
If you have your own datasets or the same datasets using a different name, check
the corresponding script (e.g., scripts/preprocessing/preprocess_conll_2009.sh for CoNLL-2009) and modify it accordingly.
To preprocess the datasets, run the script preprocess_<dataset_name>.sh from the root directory of the project. For example, for CoNLL-2009:
bash scripts/preprocessing/preprocess_conll2009.shThen, compute the vocabularies:
bash scripts/preprocessing/compute_vocabulary.shAfter running the script, you will find the preprocessed datasets in data/preprocessed/ as follows:
data/preprocessed/
├── conll2009
│ ├── ca
│ │ ├── CoNLL2009_dev.json
│ │ ├── CoNLL2009_test.json
│ │ ├── CoNLL2009_train.json
│ │ └── vocabulary.json
│ ├── cs
│ │ ├── CoNLL2009_dev.json
│ │ ├── CoNLL2009_test.json
│ │ ├── CoNLL2009_test_ood.json
│ │ ├── CoNLL2009_train.json
│ │ └── vocabulary.json
│ ├── de
│ │ ├── CoNLL2009_dev.json
...
If you have CoNLL-2009, CoNLL-2012 or OntoNotes, you can map the PropBank labels to the VerbAtlas labels (and then train a system to predict VerbAtlas labels directly).
- Preprocess the dataset(s) as described above.
- Download VerbAtlas from the official website.
- Extract the compressed folder and place it in
data/resources/, i.e.,data/resources/verbatlas-1.1. - Run the command
bash scripts/preprocessing/remap_pb2va.sh. - Rerun the command
bash scripts/preprocessing/compute_vocabulary.sh. - Run the Python script
python scripts/preprocessing/compute_candidates_from_verbatlas.py
You should now see the preprocessed VerbAtlas dataset and the label vocabulary:
data/preprocessed/
├── conll2009
...
│ ├── en
│ │ ├── CoNLL2009_dev.json
│ │ ├── CoNLL2009_dev.va.json
│ │ ├── CoNLL2009_test.json
│ │ ├── CoNLL2009_test_ood.json
│ │ ├── CoNLL2009_test.va.json
│ │ ├── CoNLL2009_train.json
│ │ ├── CoNLL2009_train.va.json
│ │ ├── vocabulary.json
│ │ └── vocabulary.va.json
...
Once you have everything ready, training a model is quite simple. Just run the command:
export PYTHONPATH="$PWD" && \
python scripts/training/trainer.py [fit|validate|test] \
--config path/to/base/configuration.yaml \
--config path/to/dataset-specific/values.yamlFor example, training on CoNLL-2009 with PropBank labels using RoBERTa-base as the underlying language model:
export PYTHONPATH="$PWD" && \
python scripts/training/trainer.py fit \
--config configurations/conll2009/base.yaml \
--config configurations/conll2009/roberta/roberta-base-ft.yamlWhere --config <configuration-file> specifies the paths to the datasets to use for training, validation, and testing, the hyperparameters to use, etc. Take a look at the available configurations in configurations/. Feel free to contribute new configurations!
If you have a checkpoint, you can evaluate the performance of the model on the default validation/development set by using the following command:
export PYTHONPATH="$PWD" && \
python scripts/training/trainer.py validate \
--config configurations/conll2009/base.yaml \
--config configurations/conll2009/roberta/roberta-base-ft.yaml \
--ckpt_path path/to/checkpoint.ckptNote: The results are just an estimate (+/- 0.1) of the official scorers. This estimates are useful to simplify the code during training, validation and debugging without having to resort to the official scorers for CoNLL-2009 and CoNLL-2005 (used for CoNLL-2012), which are written in Perl. If you want to obtain the official scores and compare them with the literature, please refer to the section below.
Or you can evaluate the model on the default test set by changing validate with test:
export PYTHONPATH="$PWD" && \
python scripts/training/trainer.py test \
--config configurations/conll2009/base.yaml \
--config configurations/conll2009/roberta/roberta-base-ft.yaml \
--ckpt_path path/to/checkpoint.ckptYou can evaluate a model on a custom dataset by specifying the path of the dataset with the --data.test_path argument:
export PYTHONPATH="$PWD" && \
python scripts/training/trainer.py test \
--config configurations/conll2009/base.yaml \
--config configurations/conll2009/roberta/roberta-base-ft.yaml \
--ckpt_path path/to/checkpoint.ckpt \
--data.test_path path/to/custom/dataset.jsonThe custom dataset must have the same format as the preprocessed datasets.
Each time you run the trainer.py script using validate or test, the script will also generate a val_predictions.json or test_predictions.json file. This file can be used to measure the performance of the model according to the official scorer of a task (e.g., CoNLL-2009).
To obtain the official scores for the CoNLL-2009 task, run:
python scripts/evaluation/evaluate_on_conll2009.py \
--gold data/original/conll2009/en/CoNLL2009_test.txt \
--predictions path/to/test_predictions.json
# Output
SYNTACTIC SCORES:
Labeled attachment score: 48711 / 48711 * 100 = 100.00 %
Unlabeled attachment score: 48711 / 48711 * 100 = 100.00 %
Label accuracy score: 48711 / 48711 * 100 = 100.00 %
Exact syntactic match: 2000 / 2000 * 100 = 100.00 %
SEMANTIC SCORES:
Labeled precision: (18170 + 8802) / (20029 + 8987) * 100 = 92.96 %
Labeled recall: (18170 + 8802) / (19949 + 8987) * 100 = 93.21 %
Labeled F1: 93.08
Unlabeled precision: (18926 + 8987) / (20029 + 8987) * 100 = 96.20 %
Unlabeled recall: (18926 + 8987) / (19949 + 8987) * 100 = 96.46 %
Unlabeled F1: 96.33
Proposition precision: 6741 / 8987 * 100 = 75.01 %
Proposition recall: 6741 / 8987 * 100 = 75.01 %
Proposition F1: 75.01
Exact semantic match: 766 / 2000 * 100 = 38.30 %
OVERALL MACRO SCORES (Wsem = 0.50):
Labeled macro precision: 96.48 %
Labeled macro recall: 96.61 %
Labeled macro F1: 96.54 %
Unlabeled macro precision: 98.10 %
Unlabeled macro recall: 98.23 %
Unlabeled macro F1: 98.17 %
Exact overall match: 766 / 2000 * 100 = 38.30 %
OVERALL MICRO SCORES:
Labeled micro precision: (48711 + 18170 + 8802) / (48711 + 20029 + 8987) * 100 = 97.37 %
Labeled micro recall: (48711 + 18170 + 8802) / (48711 + 19949 + 8987) * 100 = 97.47 %
Labeled micro F1: 97.42
Unlabeled micro precision: (48711 + 18926 + 8987) / (48711 + 20029 + 8987) * 100 = 98.58 %
Unlabeled micro recall: (48711 + 18926 + 8987) / (48711 + 19949 + 8987) * 100 = 98.68 %
Unlabeled micro F1: 98.63 Which means that model achieved about 93.1% in F1 score (Labeled F1 in SEMANTIC SCORES). The script will also create a test_predictions.conll file in the same CoNLL format as the gold file.
To obtain the official score for CoNLL-2012, run:
python scripts/evaluation/evaluate_on_conll2012.py \
--gold data/original/conll2012/en/CoNLL2012_test.txt \
--predictions predictions/conll2012/roberta-large-ft/test_predictions.json
# Output
Number of Sentences : 9263
Number of Propositions : 26715
Percentage of perfect props : 75.72
corr. excess missed prec. rec. F1
------------------------------------------------------------
Overall 54635 7467 6802 87.98 88.93 88.45
----------
ARG0 12322 816 710 93.79 94.55 94.17
ARG1 18953 1862 1638 91.05 92.05 91.55
ARG2 6405 982 888 86.71 87.82 87.26
ARG3 314 107 102 74.58 75.48 75.03
ARG4 332 77 64 81.17 83.84 82.48
ARG5 7 6 2 53.85 77.78 63.64
ARGA 0 0 2 0.00 0.00 0.00
ARGM-ADJ 164 102 92 61.65 64.06 62.84
ARGM-ADV 1700 718 796 70.31 68.11 69.19
ARGM-CAU 310 118 79 72.43 79.69 75.89
ARGM-COM 24 31 6 43.64 80.00 56.47
ARGM-DIR 278 111 163 71.47 63.04 66.99
ARGM-DIS 2308 439 356 84.02 86.64 85.31
ARGM-DSP 0 0 1 0.00 0.00 0.00
ARGM-EXT 108 73 70 59.67 60.67 60.17
ARGM-GOL 39 38 41 50.65 48.75 49.68
ARGM-LOC 1192 335 359 78.06 76.85 77.45
ARGM-LVB 66 8 6 89.19 91.67 90.41
ARGM-MNR 1183 430 367 73.34 76.32 74.80
ARGM-MOD 2210 52 29 97.70 98.70 98.20
ARGM-NEG 1194 91 29 92.92 97.63 95.22
ARGM-PNC 16 17 63 48.48 20.25 28.57
ARGM-PRD 92 163 196 36.08 31.94 33.89
ARGM-PRP 333 160 131 67.55 71.77 69.59
ARGM-REC 15 4 20 78.95 42.86 55.56
ARGM-TMP 3811 547 441 87.45 89.63 88.52
R-ARG0 594 43 45 93.25 92.96 93.10
R-ARG1 521 66 44 88.76 92.21 90.45
R-ARG2 28 10 19 73.68 59.57 65.88
R-ARG3 1 3 0 25.00 100.00 40.00
R-ARG4 1 0 2 100.00 33.33 50.00
R-ARGM-ADV 0 0 2 0.00 0.00 0.00
R-ARGM-CAU 4 2 0 66.67 100.00 80.00
R-ARGM-DIR 0 1 1 0.00 0.00 0.00
R-ARGM-EXT 1 0 0 100.00 100.00 100.00
R-ARGM-LOC 54 29 15 65.06 78.26 71.05
R-ARGM-MNR 5 5 6 50.00 45.45 47.62
R-ARGM-PRD 0 0 1 0.00 0.00 0.00
R-ARGM-PRP 0 0 2 0.00 0.00 0.00
R-ARGM-TMP 50 21 14 70.42 78.12 74.07
------------------------------------------------------------
V 26713 7 2 99.97 99.99 99.98
------------------------------------------------------------Which means that model achieved about 88.5% in F1 score (F1 in Overall).
If you have a trained model checkpoint, you can use it to generate new SRL annotations.
We provide a simple script to annotate new text, which you can use as follows:
# Command
export PYTHONPATH="$PWD" && \
python scripts/inference/predict.py \
--checkpoint logs/ontonotes/roberta-base-ft_extended-vocab/checkpoints/epoch\=06-f1\=0.8938.ckpt \
--vocabulary data/preprocessed/ontonotes/vocabulary.extended.json \
--text "The quick brown fox jumps over the lazy dog."
# Output
{'sentence_id': 0, 'words': ['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog', '.'], 'predictions': {4: {'predicate': 'jump.03', 'roles': {(0, 4): 'ARG0', (5, 9): 'ARGM-DIR'}}}}The script also takes care of sentence splitting.
export PYTHONPATH="$PWD" && \
python scripts/inference/predict.py \
--checkpoint logs/ontonotes/roberta-base-ft_extended-vocab/checkpoints/epoch\=06-f1\=0.8938.ckpt \
--vocabulary data/preprocessed/ontonotes/vocabulary.extended.json \
--text "The quick brown fox jumps over the lazy dog. Today I ate pizza. Semantic Role Labeling is the task of extracting the predicate-argument structures within a given sentence."
# Output
{'sentence_id': 0, 'words': ['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog', '.'], 'predictions': {4: {'predicate': 'jump.03', 'roles': {(0, 4): 'ARG0', (5, 9): 'ARGM-DIR'}}}}
{'sentence_id': 1, 'words': ['Today', 'I', 'ate', 'pizza', '.'], 'predictions': {2: {'predicate': 'eat.01', 'roles': {(0, 1): 'ARGM-TMP', (1, 2): 'ARG0', (3, 4): 'ARG1'}}}}
{'sentence_id': 2, 'words': ['Semantic', 'Role', 'Labeling', 'is', 'the', 'task', 'of', 'extracting', 'the', 'predicate', '-', 'argument', 'structures', 'within', 'a', 'given', 'sentence', '.'], 'predictions': {3: {'predicate': 'be.03', 'roles': {}}, 7: {'predicate': 'extract.01', 'roles': {(8, 17): 'ARG1'}}, 15: {'predicate': 'give.01', 'roles': {(16, 17): 'ARG1'}}}}And lets you specify the language of the input text (default: en):
# Command
export PYTHONPATH="$PWD" && \
python scripts/inference/predict.py \
--checkpoint logs/ontonotes/xlm-roberta-base-ft_va/checkpoints/epoch\=11-f1\=0.8875.ckpt \
--vocabulary data/preprocessed/ontonotes/vocabulary.va.extended.it.json \
--language it --text "Mi chiamo Simone. Oggi ho mangiato un gelato."
# Output
{'sentence_id': 0, 'words': ['Mi', 'chiamo', 'Simone', '.'], 'predictions': {1: {'predicate': 'NAME', 'roles': {(0, 1): 'Theme', (2, 3): 'Attribute'}}}}
{'sentence_id': 1, 'words': ['Oggi', 'ho', 'mangiato', 'un', 'gelato', '.'], 'predictions': {2: {'predicate': 'EAT_BITE', 'roles': {(0, 1): 'Time', (3, 5): 'Patient'}}}}You can also write the predictions directly to file:
export PYTHONPATH="$PWD" && \
python scripts/inference/predict.py \
--checkpoint logs/ontonotes/roberta-base-ft_extended-vocab/checkpoints/epoch\=06-f1\=0.8938.ckpt \
--vocabulary data/preprocessed/ontonotes/vocabulary.extended.json \
--text "The quick brown fox jumps over the lazy dog."
--output_file out.jsonl
# The following predictions will be stored in 'out.jsonl'.
{'sentence_id': 0, 'words': ['The', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy', 'dog', '.'], 'predictions': {4: {'predicate': 'jump.03', 'roles': {(0, 4): 'ARG0', (5, 9): 'ARGM-DIR'}}}}Under the hood, we use Stanza to preprocess the text. Thanks Stanza!
The authors gratefully acknowledge the support of the ERC Consolidator Grant MOUSSE No. 726487 under the European Union’s Horizon 2020 research and innovation programme.
This work was supported in part by the MIUR under grant “Dipartimenti di eccellenza 2018-2022” of the Department of Computer Science of Sapienza University of Rome.
This work (paper, models, checkpoints and all the contents of this repository) are licensed under Creative Commons Attribution-NonCommercial 4.0 International.
See LICENSE.txt for more details.