SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Ousidhoum, Nedjma Muhammad, Shamsuddeen Hassan Abdalla, Mohamed Abdulmumin, Idris Ahmad, Ibrahim Said Ahuja, Sanchit Aji, Alham Fikri Araujo, Vladimir Ayele, Abinew Ali Baswani, Pavan Beloucif, Meriem Biemann, Chris Bourhim, Sofia De Kock, Christine Dekebo, Genet Shanko Hourrane, Oumaima Kanumolu, Gopichand Madasu, Lokesh Rutunda, Samuel Shrivastava, Manish Solorio, Thamar Surange, Nirmal Tilaye, Hailegnaw Getaneh Vishnubhotla, Krishnapriya Winata, Genta Yimam, Seid Muhie Mohammad, Saif M. |
| author_facet | Ousidhoum, Nedjma Muhammad, Shamsuddeen Hassan Abdalla, Mohamed Abdulmumin, Idris Ahmad, Ibrahim Said Ahuja, Sanchit Aji, Alham Fikri Araujo, Vladimir Ayele, Abinew Ali Baswani, Pavan Beloucif, Meriem Biemann, Chris Bourhim, Sofia De Kock, Christine Dekebo, Genet Shanko Hourrane, Oumaima Kanumolu, Gopichand Madasu, Lokesh Rutunda, Samuel Shrivastava, Manish Solorio, Thamar Surange, Nirmal Tilaye, Hailegnaw Getaneh Vishnubhotla, Krishnapriya Winata, Genta Yimam, Seid Muhie Mohammad, Saif M. |
| contents | Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present \textit{SemRel}, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: \textit{Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish,} and \textit{Telugu}. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia -- regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages Ousidhoum, Nedjma Muhammad, Shamsuddeen Hassan Abdalla, Mohamed Abdulmumin, Idris Ahmad, Ibrahim Said Ahuja, Sanchit Aji, Alham Fikri Araujo, Vladimir Ayele, Abinew Ali Baswani, Pavan Beloucif, Meriem Biemann, Chris Bourhim, Sofia De Kock, Christine Dekebo, Genet Shanko Hourrane, Oumaima Kanumolu, Gopichand Madasu, Lokesh Rutunda, Samuel Shrivastava, Manish Solorio, Thamar Surange, Nirmal Tilaye, Hailegnaw Getaneh Vishnubhotla, Krishnapriya Winata, Genta Yimam, Seid Muhie Mohammad, Saif M. Computation and Language Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present \textit{SemRel}, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: \textit{Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish,} and \textit{Telugu}. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia -- regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP. |
| title | SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.08638 |