UINAUIL

UINAUIL (Unified Interactive Natural Understanding of the Italian Language) is a Python module for downloading language resources in order to train, evaluate, and analyze natural language understanding systems for the Italian Laguage, inspired by the GLUE and SuperGLUE benchmarks. It is based on the European Language Grid (ELG) platform, that provides access to Language Technology services and resources from all over Europe.

Installation

The uinauil module can be installed via pip / PyPI with:

pip install uinauil

Otherwise, you can directly download the source file and save it on your working folder.

Dependencies

The project dependencies are:

• python 3.6.7
• elg 0.4.22
• scikit_learn 1.2.1

You can install the project requirements with:

pip install -r requirement.txt

Quick start

Here the basic functionalities of the uinauil package:

1. select a task;
2. download training e test set of the task;
3. evaluate the predictions of a model on the standard metrics of the task.

Note: the uinauil package does not contain models, but only datasets for training your models on common tasks.

import uinauil as ul

# load a task, for example 'facta'
task = ul.Task('facta')

# get training and test set of the task
train = task.data.training_set   # training set
test = task.data.test_set        # test set

# train your model on the training set and make prediction on test set
...
predctions = <make predictions on test set>

# evaluate your model on the standard metrics of the task
scores = task.evaluate(predictions)
print(scores)

You can see an example of this code on the Quick Start notebook.

Usage

Here we introduce all the functionalities of the uinauil package.

First of all, import the package with:

import uinauil as ul

For getting the list of available tasks use:

ul.tasks

tasks contains a dictionary of the available tasks (here the list with a brief presentation). Each key is the name of the task, while the value contains its identifier on the ELG platform and the type of task.

Select a task

You can select a task (for example 'facta') using the Task method:

task = ul.Task('facta')

This instruction downloads the task data on your local computer into the ./data folder. In order to download the resources you need to use your ELG account.

The task object contains 4 instance variables:

• task_name: name of the task (the same used as key in ul.tasks);
• desc: description of the task from the ELG platform;
• link: link to the original resource on the ELG platform;
• data: dataset of the task, the main content described in the next section.

The data variable

The data variable of Task object contains the dataset, already divided into training and test sets:

train = task.data.training_set   # training set
test = task.data.test_set        # test set

Both training and test sets are retrieved in JSON format (a dictionary in Python). Each task uses a different set of keys, then the data variable is also provided with several variables containing useful metadata:

• data.keys: list of all the keys in the dictionary;
• data.feature_keys: list of keys of the features;
• data.target_key: key of the target;
• data.feature_dim: list of dimensionality of each feature (single or multiple), in the order of data.feature_keys;
• data.target_dim: dimensionality of target (single or multiple);
• data.feature_info: list of brief descriptions of the meaning of each feature, in the order of data.feature_keys;
• data.target_values: list of possible values of the target;
• data.target_desc: list of brief descriptions of the meaning of each possible value of the target, in the order of data.target_values.

You can see a sample of all the available metadata or each task on the Metadata notebook.

Train a model

Sorry, uinaiul does not contain any pre-trained model. You have to use an external package for building a model, then you can train it using the training set of a task.

Evaluate a model

The evaluate method of the task object takes a list of predictions on the test set and calculates the standard performance metrics on the task.

scores = task.evaluate(predictions)
print(scores)

The metrics have been chosen according to the type of task (described here):

• for sequence tasks, the method returns the accuracy, as the ratio of hits over all the tokens;
• for classification tasks, the method returns accuracy on all classes, and precision, recall and F1 for each single class and their macro average;
• for pairs tasks, the method returns the classification report from scikit-learn.

Details on tasks

The uinauil module contains 6 tasks, divides into the following 3 types:

• sequence type, where the features set is composed by a list of tokens. It consists of the following 2 tasks:
  • facta: the FACTA dataset consists of 169 news stories selected from the Ita-TimeBank, 120 Wikinews articles, and 301 tweets, annotated with event factuality information.
  • eventi: the EVENTI corpus collects news articles and stories annotated with temporal information at different levels (i.e. events, temporal expressions, signals and temporal relations) following the It-TimeML Annotation Guidelines.
• classification type, where the features set is composed by a single text. It consists of the following 3 tasks:
  • haspeede: the HaSpeeDe2 dataset collects 8,012 tweets and 500 news headlines annotated for the presence of hate speech, stereotypes and nominal utterance.
  • ironita: the IronITA dataset collects 4,849 tweets annotated for irony and sarcasm.
  • sentipolc: The SENTIPOLC 2016 dataset contains 9410 tweets annotated for subjectivity, overall and literal polarity, and irony.
• pairs type, where the features set is composed by a pair of texts. It consists of the following task:
  • textualentailment: the Textual Entailment dataset contains 800 pairs of Italian sentences, extracted from Wikipedia, and annotated for the presence of textual entailment.

Working examples

The examples folder in this project contains several examples of use of the uinauil module:

• Quick Start: presents the main feature of uinauil;
• Metadata: shows the values of the metadata for each task.

Leaderboard

As a baseline we used the training and test sets of each task to train and evaluate common ML models. The results of the best models are summarized in the following table:

Rank	Creator	Model Name	AVG	facta	eventi	haspeede	ironita	sentipolc	textualentailment
1		Italian BERT XXL	.769	.908	.936	.791	.765	.675	.541
2		Italian BERT	.755	.907	.916	.785	.736	.646	.538
3		ALBERTO	.744	.909	.925	.741	.742	.621	.529
4		Multilingual BERT	.731	.909	.925	.739	.709	.559	.544

The leaderboards of the models on each sigle task are available on the Leaderboards file.

Include a new model

If you want to add your model to the ranking, please contact Valerio Basile.

Authors

The uinauil package has been created by Valerio Basile, with the collaboration of Alessio Bosca and Livio Bioglio.

Citation

If you use uinauil in a scientific publication, we would appreciate citations of our paper:

@inproceedings{basile-etal-2023-uinauil,
    title = "{UINAUIL}: A Unified Benchmark for {I}talian Natural Language Understanding",
    author = "Basile, Valerio  and
      Bioglio, Livio  and
      Bosca, Alessio  and
      Bosco, Cristina  and
      Patti, Viviana",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.33",
    doi = "10.18653/v1/2023.acl-demo.33",
    pages = "348--356"
}

License

GNU General Public License v3.0