Datasets:
metadata
language:
- afr
- amh
- arb
- arq
- ary
- eng
- es
- hau
- hin
- ind
- kin
- mar
- pan
- tel
dataset_info:
- config_name: afr
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
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- name: dev
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download_size: 95864
dataset_size: 131492
- config_name: amh
features:
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dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
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- name: test
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- name: dev
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- config_name: arb
features:
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- name: label
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- config_name: arq
features:
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- name: sentence2
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- name: label
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- config_name: ary
features:
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- name: sentence2
dtype: string
- name: label
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splits:
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- config_name: eng
features:
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- config_name: esp
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- name: test
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- config_name: hau
features:
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- config_name: hin
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- name: label
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- name: dev
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- config_name: ind
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- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
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- name: dev
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download_size: 68263
dataset_size: 94764
- config_name: kin
features:
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dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
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- name: test
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- name: dev
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dataset_size: 332489
- config_name: mar
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- name: sentence2
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- name: label
dtype: float64
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- name: test
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- name: dev
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- config_name: pan
features:
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- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
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- name: dev
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num_examples: 242
download_size: 166402
dataset_size: 425385
- config_name: tel
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float64
splits:
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- name: test
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num_examples: 297
- name: dev
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num_examples: 130
download_size: 347275
dataset_size: 771712
configs:
- config_name: afr
data_files:
- split: test
path: afr/test-*
- split: dev
path: afr/dev-*
- config_name: amh
data_files:
- split: train
path: amh/train-*
- split: test
path: amh/test-*
- split: dev
path: amh/dev-*
- config_name: arb
data_files:
- split: test
path: arb/test-*
- split: dev
path: arb/dev-*
- config_name: arq
data_files:
- split: train
path: arq/train-*
- split: test
path: arq/test-*
- split: dev
path: arq/dev-*
- config_name: ary
data_files:
- split: train
path: ary/train-*
- split: test
path: ary/test-*
- split: dev
path: ary/dev-*
- config_name: eng
data_files:
- split: train
path: eng/train-*
- split: test
path: eng/test-*
- split: dev
path: eng/dev-*
- config_name: esp
data_files:
- split: train
path: esp/train-*
- split: test
path: esp/test-*
- split: dev
path: esp/dev-*
- config_name: hau
data_files:
- split: train
path: hau/train-*
- split: test
path: hau/test-*
- split: dev
path: hau/dev-*
- config_name: hin
data_files:
- split: test
path: hin/test-*
- split: dev
path: hin/dev-*
- config_name: ind
data_files:
- split: test
path: ind/test-*
- split: dev
path: ind/dev-*
- config_name: kin
data_files:
- split: train
path: kin/train-*
- split: test
path: kin/test-*
- split: dev
path: kin/dev-*
- config_name: mar
data_files:
- split: train
path: mar/train-*
- split: test
path: mar/test-*
- split: dev
path: mar/dev-*
- config_name: pan
data_files:
- split: test
path: pan/test-*
- split: dev
path: pan/dev-*
- config_name: tel
data_files:
- split: train
path: tel/train-*
- split: test
path: tel/test-*
- split: dev
path: tel/dev-*
task_categories:
- text-classification
- sentence-similarity
Dataset Description
- Homepage: https://semantic-textual-relatedness.github.io
- Repository: GitHub
- Paper: SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
- Paper: SemEval Task 1: Semantic Textual Relatedness for African and Asian Languages
- Leaderboard: https://codalab.lisn.upsaclay.fr/competitions/16799#results
- Point of Contact: Nedjma Ousidhoum,Shamsuddeen Hassan Muhammad
Dataset Summary
SemRel2024 is a collection of Semantic Textual Relatedness (STR) datasets for 14 languages, including African and Asian languages. The dataset is designed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The task aims to evaluate the ability of systems to measure the semantic relatedness between two text segments, such as sentences or phrases.
Supported Tasks and Leaderboards
The SemRel2024 dataset can be used for the Semantic Textual Relatedness task, which involves predicting the degree of semantic relatedness between two text segments on a scale, typically from 0 (not related at all) to 5 (highly related).
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
Languages
The SemRel2024 dataset covers the following 14 languages:
- Afrikaans (afr)
- Algerian Arabic (arq)
- Amharic (amh)
- English (eng)
- Hausa (hau)
- Indonesian (ind)
- Hindi (hin)
- Kinyarwanda (kin)
- Marathi (mar)
- Modern Standard Arabic (arb)
- Moroccan Arabic (ary)
- Punjabi (pan)
- Spanish (esp)
- Telugu (tel)
Dataset Structure
Data Instances
Each instance in the dataset consists of two text segments and a relatedness score indicating the degree of semantic relatedness between them.
{ "text1": "string", "text2": "string", "score": float }
- text1: a string feature representing the first text segment.
- text2: a string feature representing the second text segment.
- score: a float value representing the semantic relatedness score between text1 and text2, typically ranging from 0 (not related at all) to 5 (highly related).
Citation Information
If you use the SemRel2024 dataset in your research, please cite the following papers:
@misc{ousidhoum2024semrel2024,
title={SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages},
author={Nedjma Ousidhoum and Shamsuddeen Hassan Muhammad and Mohamed Abdalla and Idris Abdulmumin and Ibrahim Said Ahmad and
Sanchit Ahuja and Alham Fikri Aji and Vladimir Araujo and Abinew Ali Ayele and Pavan Baswani and Meriem Beloucif and
Chris Biemann and Sofia Bourhim and Christine De Kock and Genet Shanko Dekebo and
Oumaima Hourrane and Gopichand Kanumolu and Lokesh Madasu and Samuel Rutunda and Manish Shrivastava and
Thamar Solorio and Nirmal Surange and Hailegnaw Getaneh Tilaye and Krishnapriya Vishnubhotla and Genta Winata and
Seid Muhie Yimam and Saif M. Mohammad},
year={2024},
eprint={2402.08638},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@inproceedings{ousidhoum-etal-2024-semeval,
title = "{S}em{E}val-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages",
author = "Ousidhoum, Nedjma and Muhammad, Shamsuddeen Hassan and Abdalla, Mohamed and Abdulmumin, Idris and
Ahmad,Ibrahim Said and Ahuja, Sanchit and Aji, Alham Fikri and Araujo, Vladimir and Beloucif, Meriem and
De Kock, Christine and Hourrane, Oumaima and Shrivastava, Manish and Solorio, Thamar and Surange, Nirmal and
Vishnubhotla, Krishnapriya and Yimam, Seid Muhie and Mohammad, Saif M.",
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
year = "2024",
publisher = "Association for Computational Linguistics"
}