The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: ImportError
Message: To be able to use GEM/SIMPITIKI, you need to install the following dependency: lxml.
Please install it using 'pip install lxml' for instance.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1880, in dataset_module_factory
return HubDatasetModuleFactoryWithScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1504, in get_module
local_imports = _download_additional_modules(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 354, in _download_additional_modules
raise ImportError(
ImportError: To be able to use GEM/SIMPITIKI, you need to install the following dependency: lxml.
Please install it using 'pip install lxml' for instance.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for GEM/SIMPITIKI
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
SIMPITIKI is an Italian Simplification dataset. Its examples were selected from Italian Wikipedia such that their editing tracking descriptions contain any of the words "Simplified"/"Simplify"/"Simplification".
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/SIMPITIKI')
The data loader can be found here.
website
paper
authors
Sara Tonelli (Fondazione Bruno Kessler), Alessio Palmero Aprosio (Fondazione Bruno Kessler), Francesca Saltori (Fondazione Bruno Kessler)
Dataset Overview
Where to find the Data and its Documentation
Webpage
Download
Paper
BibTex
@article{tonelli2016simpitiki,
title={SIMPITIKI: a Simplification corpus for Italian},
author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca},
journal={Proceedings of CLiC-it},
year={2016}
}
Contact Name
Sara Tonelli
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
no
Covered Dialects
None
Covered Languages
Italian
License
cc-by-4.0: Creative Commons Attribution 4.0 International
Intended Use
The purpose of the dataset is to train NLG models to simplify complex text by learning different types of transformations (verb to noun, noun to verbs, deletion, insertion, etc)
Primary Task
Simplification
Communicative Goal
This dataset aims to enhance research in text simplification in Italian language with different text transformations.
Credit
Curation Organization Type(s)
academic, independent
Curation Organization(s)
Fondazione Bruno Kessler (FBK)
Dataset Creators
Sara Tonelli (Fondazione Bruno Kessler), Alessio Palmero Aprosio (Fondazione Bruno Kessler), Francesca Saltori (Fondazione Bruno Kessler)
Funding
EU Horizon 2020 Programme via the SIMPATICO Project (H2020-EURO-6-2015, n. 692819)
Who added the Dataset to GEM?
Sebastien Montella (Orange Labs), Vipul Raheja (Grammarly Inc.)
Dataset Structure
Data Fields
Each sample comes with the following fields:
gem_id(string): Unique sample ID -text(string): The raw text to be simplified -simplified_text(string): The simplified version of "text" field -transformation_type(string): Nature of transformation applied to raw text in order to simplify it. -source_dataset(string): Initial dataset source of sample. Values: 'itwiki' (for Italian Wikipedia) or 'tn' (manually annotated administrative documents from the Municipality of Trento, Italy)
Reason for Structure
The dataset is organized as a pairs where the raw text (input) is associated with its simplified text (output). The editing transformation and the source dataset of each sample is also provided for advanced analysis.
How were labels chosen?
SIMPITIKI dataset selects documents from Italian Wikipedia such that their editing tracking descriptions contain any of the words "Simplified"/"Simplify"/"Simplification". For the Public Administration domain of the documents of the Municipality of Trento (Italy)
Example Instance
{"transformation_id": 31, "transformation_type": "Transformation - Lexical Substitution (word level)", "source_dataset": "tn", "text": "- assenza per <del>e</del>si<del>genze</del> particolari attestate da relazione dei servizi sociali;", "simplified_text": "- assenza per <ins>bi</ins>s<ins>ogn</ins>i particolari attestati da relazione dei servizi sociali;"}
Data Splits
Several splits are proposed to train models on different configurations:
-"train": Training samples randomly selected from initial corpus. 816 training samples. -"validation": Validating samples randomly selected from initial corpus. 174 validating samples. -"test": Testing samples randomly selected from initial corpus. 176 validating samples. -"challenge_seen_transformations_train": This training challenge split includes specific transformations to simplify the raw text. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 562 training samples. -"challenge_seen_transformations_val": This validating challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 121 validating samples. -"challenge_seen_transformations_test": This testing challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 127 testing samples. -"challenge_unseen_transformations_test" : "Insert - Subject", "Delete - Subject", "Transformation - Lexical Substitution (phrase level)", "Transformation - Verb to Noun (nominalization)", "Transformation - Verbal Voice". 356 testing samples. -"challenge_itwiki_train": This challenge split includes random samples from the Italian Wikipedia as source dataset. 402 training samples. -"challenge_itwiki_val": This validating challenge split includes random samples from the Italian Wikipedia as source dataset. 86 validating samples. -"challenge_itwiki_test": This testing challenge split includes random samples from the Italian Wikipedia as source dataset. 87 testing samples. -"challenge_tn_test": This testing challenge split includes all samples from the Municipality of Trento administrative documents ('tn') as source dataset. 591 testing samples.
Splitting Criteria
The training ratio is set to 0.7. The validation and test somehow equally divide the remaining 30% of the dataset.
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
This dataset promotes Simplification task for Italian language.
Similar Datasets
no
Ability that the Dataset measures
Models can be evaluated if they can simplify text regarding different simplification transformations.
GEM-Specific Curation
Modificatied for GEM?
yes
Additional Splits?
yes
Split Information
The SIMPITIKI dataset provides a single file. Several splits are proposed to train models on different configurations: -"train": Training samples randomly selected from initial corpus. 816 training samples. -"validation": Validating samples randomly selected from initial corpus. 174 validating samples. -"test": Testing samples randomly selected from initial corpus. 176 validating samples. -"challenge_seen_transformations_train": This training challenge split includes specific transformations to simplify the raw text. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 562 training samples. -"challenge_seen_transformations_val": This validating challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 121 validating samples. -"challenge_seen_transformations_test": This testing challenge split includes same transformations than the ones observed in training. Precisely, transformations are "Split", "Merge", "Reordering", "Insert - Verb", "Insert - Other", "Delete - Verb", "Delete - Other", "Transformation - Lexical Substitution (word level)", "Transformation - Anaphoric replacement", "Transformation - Noun to Verb", "Transformation - Verbal Features". 127 testing samples. -"challenge_unseen_transformations_test" : "Insert - Subject", "Delete - Subject", "Transformation - Lexical Substitution (phrase level)", "Transformation - Verb to Noun (nominalization)", "Transformation - Verbal Voice". 356 testing samples. -"challenge_itwiki_train": This challenge split includes random samples from the Italian Wikipedia as source dataset. 402 training samples. -"challenge_itwiki_val": This validating challenge split includes random samples from the Italian Wikipedia as source dataset. 86 validating samples. -"challenge_itwiki_test": This testing challenge split includes random samples from the Italian Wikipedia as source dataset. 87 testing samples. -"challenge_tn_test": This testing challenge split includes all samples from the Municipality of Trento administrative documents ('tn') as source dataset. 591 testing samples.
Split Motivation
The splits allows to investigate the generalization of models regarding editing/transformations ("challenge_seen_transformations_test" / "challenge_unseen_transformations_test") and for transfer learning to different domain ("challenge_tn_test")
Getting Started with the Task
Pointers to Resources
- Coster and Kauchak, Simple English Wikipedia: A New Text Simplification Task, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 665–669, Portland, Oregon, June 19-24, 2011
- Xu et al, Optimizing Statistical Machine Translation for Text Simplification, Transactions of the Association for Computational Linguistics, vol. 4, pp. 401–415, 2016
- Aprosio et al, Neural Text Simplification in Low-Resource Conditions Using Weak Supervision, Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation (NeuralGen), pages 37–44, Minneapolis, Minnesota, USA, June 6, 2019
Technical Terms
Simplification: Process that consists in transforming an input text to its simplified version.
Previous Results
Previous Results
Measured Model Abilities
The splits allows to investigate the generalization of models regarding editing/transformations ("challenge_seen_transformations_test" / "challenge_unseen_transformations_test") and for transfer learning to different domain ("challenge_tn_test")
Metrics
BLEU, Other: Other Metrics
Other Metrics
FKBLEU (https://aclanthology.org/Q16-1029.pdf): Combines Flesch-Kincaid Index and iBLEU metrics. SARI (https://aclanthology.org/Q16-1029.pdf): Compares system output against references and against the input sentence. It explicitly measures the goodness of words that are added, deleted and kept by the systems Word-level F1
Previous results available?
no
Dataset Curation
Original Curation
Original Curation Rationale
Most of the resources for Text Simplification are in English. To stimulate research to different languages, SIMPITIKI proposes an Italian corpus with Complex-Simple sentence pairs.
Communicative Goal
Text simplification allows a smooth reading of text to enhance understanding.
Sourced from Different Sources
yes
Source Details
Italian Wikipedia (Manually) Annotated administrative documents from the Municipality of Trento, Italy
Language Data
How was Language Data Obtained?
Found
Where was it found?
Single website, Offline media collection
Language Producers
SIMPITIKI is a combination of documents from Italian Wikipedia and from the Municipality of Trento, Italy.
Topics Covered
Samples from documents from the Municipality of Trento corpus are in the administrative domain.
Data Validation
validated by data curator
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
crowd-sourced
Number of Raters
unknown
Rater Qualifications
Native speaker
Raters per Training Example
0
Raters per Test Example
0
Annotation Service?
unknown
Annotation Values
Annotators specified any of the tags as designed by Brunato et al. (https://aclanthology.org/W15-1604/): -Split: Splitting a clause into two clauses. -Merge: Merge two or more clauses together. -Reordering: Word order changes. -Insert: Insertion of words or phrases that provide supportive information to the original sentence -Delete: dropping redundant information. -Transformation: Modification which can affect the sentence at the lexical, morpho-syntactic and syntactic level: Lexical substitution (word level) / Lexical substitution (phrase level) / Anaphoric replacement / Noun to Verb / Verb to Noun / Verbal voice / Verbal features/ morpho–syntactic and syntactic level, also giving rise to overlapping phenomena
Any Quality Control?
unknown
Consent
Any Consent Policy?
no
Justification for Using the Data
The dataset is available online under the CC-BY 4.0 license.
Private Identifying Information (PII)
Contains PII?
likely
Categories of PII
generic PII
Any PII Identification?
no identification
Maintenance
Any Maintenance Plan?
no
Broader Social Context
Previous Work on the Social Impact of the Dataset
Usage of Models based on the Data
no
Impact on Under-Served Communities
Addresses needs of underserved Communities?
yes
Details on how Dataset Addresses the Needs
The creator of SIMPITIKI wants to promote text simplification for Italian because few resources are available in other languages than English.
Discussion of Biases
Any Documented Social Biases?
unsure
Considerations for Using the Data
PII Risks and Liability
Licenses
Copyright Restrictions on the Dataset
research use only
Copyright Restrictions on the Language Data
research use only
Known Technical Limitations
Discouraged Use Cases
The risk of surface-based metrics (BLEU, chrf++, etc) for this task is that semantic adequacy is not respected when simplifying the input document.
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