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1. The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Author

Laurençon, Hugo, Saulnier, Lucile, Wang, Thomas, Akiki, Christopher, del Moral, Albert Villanova, Scao, Teven Le, Von Werra, Leandro, Mou, Chenghao, Ponferrada, Eduardo González, Nguyen, Huu, Frohberg, Jörg, Šaško, Mario, Lhoest, Quentin, McMillan-Major, Angelina, Dupont, Gerard, Biderman, Stella, Rogers, Anna, allal, Loubna Ben, De Toni, Francesco, Pistilli, Giada, Nguyen, Olivier, Nikpoor, Somaieh, Masoud, Maraim, Colombo, Pierre, de la Rosa, Javier, Villegas, Paulo, Thrush, Tristan, Longpre, Shayne, Nagel, Sebastian, Weber, Leon, Muñoz, Manuel, Zhu, Jian, Van Strien, Daniel, Alyafeai, Zaid, Almubarak, Khalid, Vu, Minh Chien, Gonzalez-Dios, Itziar, Soroa, Aitor, Lo, Kyle, Dey, Manan, Suarez, Pedro Ortiz, Gokaslan, Aaron, Bose, Shamik, Adelani, David, Phan, Long, Tran, Hieu, Yu, Ian, Pai, Suhas, Chim, Jenny, Lepercq, Violette, Ilic, Suzana, Mitchell, Margaret, Luccioni, Sasha Alexandra, and Jernite, Yacine

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7
Abstract

As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus., Comment: NeurIPS 2022, Datasets and Benchmarks Track

Published
2023

2. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Author

Workshop, BigScience, Scao, Teven Le, Fan, Angela, Akiki, Christopher, Pavlick, Ellie, Ilić, Suzana, Hesslow, Daniel, Castagné, Roman, Luccioni, Alexandra Sasha, Yvon, François, Gallé, Matthias, Tow, Jonathan, Rush, Alexander M., Biderman, Stella, Webson, Albert, Ammanamanchi, Pawan Sasanka, Wang, Thomas, Sagot, Benoît, Muennighoff, Niklas, del Moral, Albert Villanova, Ruwase, Olatunji, Bawden, Rachel, Bekman, Stas, McMillan-Major, Angelina, Beltagy, Iz, Nguyen, Huu, Saulnier, Lucile, Tan, Samson, Suarez, Pedro Ortiz, Sanh, Victor, Laurençon, Hugo, Jernite, Yacine, Launay, Julien, Mitchell, Margaret, Raffel, Colin, Gokaslan, Aaron, Simhi, Adi, Soroa, Aitor, Aji, Alham Fikri, Alfassy, Amit, Rogers, Anna, Nitzav, Ariel Kreisberg, Xu, Canwen, Mou, Chenghao, Emezue, Chris, Klamm, Christopher, Leong, Colin, van Strien, Daniel, Adelani, David Ifeoluwa, Radev, Dragomir, Ponferrada, Eduardo González, Levkovizh, Efrat, Kim, Ethan, Natan, Eyal Bar, De Toni, Francesco, Dupont, Gérard, Kruszewski, Germán, Pistilli, Giada, Elsahar, Hady, Benyamina, Hamza, Tran, Hieu, Yu, Ian, Abdulmumin, Idris, Johnson, Isaac, Gonzalez-Dios, Itziar, de la Rosa, Javier, Chim, Jenny, Dodge, Jesse, Zhu, Jian, Chang, Jonathan, Frohberg, Jörg, Tobing, Joseph, Bhattacharjee, Joydeep, Almubarak, Khalid, Chen, Kimbo, Lo, Kyle, Von Werra, Leandro, Weber, Leon, Phan, Long, allal, Loubna Ben, Tanguy, Ludovic, Dey, Manan, Muñoz, Manuel Romero, Masoud, Maraim, Grandury, María, Šaško, Mario, Huang, Max, Coavoux, Maximin, Singh, Mayank, Jiang, Mike Tian-Jian, Vu, Minh Chien, Jauhar, Mohammad A., Ghaleb, Mustafa, Subramani, Nishant, Kassner, Nora, Khamis, Nurulaqilla, Nguyen, Olivier, Espejel, Omar, de Gibert, Ona, Villegas, Paulo, Henderson, Peter, Colombo, Pierre, Amuok, Priscilla, Lhoest, Quentin, Harliman, Rheza, Bommasani, Rishi, López, Roberto Luis, Ribeiro, Rui, Osei, Salomey, Pyysalo, Sampo, Nagel, Sebastian, Bose, Shamik, Muhammad, Shamsuddeen Hassan, Sharma, Shanya, Longpre, Shayne, Nikpoor, Somaieh, Silberberg, Stanislav, Pai, Suhas, Zink, Sydney, Torrent, Tiago Timponi, Schick, Timo, Thrush, Tristan, Danchev, Valentin, Nikoulina, Vassilina, Laippala, Veronika, Lepercq, Violette, Prabhu, Vrinda, Alyafeai, Zaid, Talat, Zeerak, Raja, Arun, Heinzerling, Benjamin, Si, Chenglei, Taşar, Davut Emre, Salesky, Elizabeth, Mielke, Sabrina J., Lee, Wilson Y., Sharma, Abheesht, Santilli, Andrea, Chaffin, Antoine, Stiegler, Arnaud, Datta, Debajyoti, Szczechla, Eliza, Chhablani, Gunjan, Wang, Han, Pandey, Harshit, Strobelt, Hendrik, Fries, Jason Alan, Rozen, Jos, Gao, Leo, Sutawika, Lintang, Bari, M Saiful, Al-shaibani, Maged S., Manica, Matteo, Nayak, Nihal, Teehan, Ryan, Albanie, Samuel, Shen, Sheng, Ben-David, Srulik, Bach, Stephen H., Kim, Taewoon, Bers, Tali, Fevry, Thibault, Neeraj, Trishala, Thakker, Urmish, Raunak, Vikas, Tang, Xiangru, Yong, Zheng-Xin, Sun, Zhiqing, Brody, Shaked, Uri, Yallow, Tojarieh, Hadar, Roberts, Adam, Chung, Hyung Won, Tae, Jaesung, Phang, Jason, Press, Ofir, Li, Conglong, Narayanan, Deepak, Bourfoune, Hatim, Casper, Jared, Rasley, Jeff, Ryabinin, Max, Mishra, Mayank, Zhang, Minjia, Shoeybi, Mohammad, Peyrounette, Myriam, Patry, Nicolas, Tazi, Nouamane, Sanseviero, Omar, von Platen, Patrick, Cornette, Pierre, Lavallée, Pierre François, Lacroix, Rémi, Rajbhandari, Samyam, Gandhi, Sanchit, Smith, Shaden, Requena, Stéphane, Patil, Suraj, Dettmers, Tim, Baruwa, Ahmed, Singh, Amanpreet, Cheveleva, Anastasia, Ligozat, Anne-Laure, Subramonian, Arjun, Névéol, Aurélie, Lovering, Charles, Garrette, Dan, Tunuguntla, Deepak, Reiter, Ehud, Taktasheva, Ekaterina, Voloshina, Ekaterina, Bogdanov, Eli, Winata, Genta Indra, Schoelkopf, Hailey, Kalo, Jan-Christoph, Novikova, Jekaterina, Forde, Jessica Zosa, Clive, Jordan, Kasai, Jungo, Kawamura, Ken, Hazan, Liam, Carpuat, Marine, Clinciu, Miruna, Kim, Najoung, Cheng, Newton, Serikov, Oleg, Antverg, Omer, van der Wal, Oskar, Zhang, Rui, Zhang, Ruochen, Gehrmann, Sebastian, Mirkin, Shachar, Pais, Shani, Shavrina, Tatiana, Scialom, Thomas, Yun, Tian, Limisiewicz, Tomasz, Rieser, Verena, Protasov, Vitaly, Mikhailov, Vladislav, Pruksachatkun, Yada, Belinkov, Yonatan, Bamberger, Zachary, Kasner, Zdeněk, Rueda, Alice, Pestana, Amanda, Feizpour, Amir, Khan, Ammar, Faranak, Amy, Santos, Ana, Hevia, Anthony, Unldreaj, Antigona, Aghagol, Arash, Abdollahi, Arezoo, Tammour, Aycha, HajiHosseini, Azadeh, Behroozi, Bahareh, Ajibade, Benjamin, Saxena, Bharat, Ferrandis, Carlos Muñoz, McDuff, Daniel, Contractor, Danish, Lansky, David, David, Davis, Kiela, Douwe, Nguyen, Duong A., Tan, Edward, Baylor, Emi, Ozoani, Ezinwanne, Mirza, Fatima, Ononiwu, Frankline, Rezanejad, Habib, Jones, Hessie, Bhattacharya, Indrani, Solaiman, Irene, Sedenko, Irina, Nejadgholi, Isar, Passmore, Jesse, Seltzer, Josh, Sanz, Julio Bonis, Dutra, Livia, Samagaio, Mairon, Elbadri, Maraim, Mieskes, Margot, Gerchick, Marissa, Akinlolu, Martha, McKenna, Michael, Qiu, Mike, Ghauri, Muhammed, Burynok, Mykola, Abrar, Nafis, Rajani, Nazneen, Elkott, Nour, Fahmy, Nour, Samuel, Olanrewaju, An, Ran, Kromann, Rasmus, Hao, Ryan, Alizadeh, Samira, Shubber, Sarmad, Wang, Silas, Roy, Sourav, Viguier, Sylvain, Le, Thanh, Oyebade, Tobi, Le, Trieu, Yang, Yoyo, Nguyen, Zach, Kashyap, Abhinav Ramesh, Palasciano, Alfredo, Callahan, Alison, Shukla, Anima, Miranda-Escalada, Antonio, Singh, Ayush, Beilharz, Benjamin, Wang, Bo, Brito, Caio, Zhou, Chenxi, Jain, Chirag, Xu, Chuxin, Fourrier, Clémentine, Periñán, Daniel León, Molano, Daniel, Yu, Dian, Manjavacas, Enrique, Barth, Fabio, Fuhrimann, Florian, Altay, Gabriel, Bayrak, Giyaseddin, Burns, Gully, Vrabec, Helena U., Bello, Imane, Dash, Ishani, Kang, Jihyun, Giorgi, John, Golde, Jonas, Posada, Jose David, Sivaraman, Karthik Rangasai, Bulchandani, Lokesh, Liu, Lu, Shinzato, Luisa, de Bykhovetz, Madeleine Hahn, Takeuchi, Maiko, Pàmies, Marc, Castillo, Maria A, Nezhurina, Marianna, Sänger, Mario, Samwald, Matthias, Cullan, Michael, Weinberg, Michael, De Wolf, Michiel, Mihaljcic, Mina, Liu, Minna, Freidank, Moritz, Kang, Myungsun, Seelam, Natasha, Dahlberg, Nathan, Broad, Nicholas Michio, Muellner, Nikolaus, Fung, Pascale, Haller, Patrick, Chandrasekhar, Ramya, Eisenberg, Renata, Martin, Robert, Canalli, Rodrigo, Su, Rosaline, Su, Ruisi, Cahyawijaya, Samuel, Garda, Samuele, Deshmukh, Shlok S, Mishra, Shubhanshu, Kiblawi, Sid, Ott, Simon, Sang-aroonsiri, Sinee, Kumar, Srishti, Schweter, Stefan, Bharati, Sushil, Laud, Tanmay, Gigant, Théo, Kainuma, Tomoya, Kusa, Wojciech, Labrak, Yanis, Bajaj, Yash Shailesh, Venkatraman, Yash, Xu, Yifan, Xu, Yingxin, Xu, Yu, Tan, Zhe, Xie, Zhongli, Ye, Zifan, Bras, Mathilde, Belkada, Younes, and Wolf, Thomas

Subjects
Computer Science - Computation and Language
Abstract

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Published
2022

3. Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets

Author

Altaher, Yousef, Fadel, Ali, Alotaibi, Mazen, Alyazidi, Mazen, Al-Mutairi, Mishari, Aldhbuiub, Mutlaq, Mosaibah, Abdulrahman, Rezk, Abdelrahman, Alhendi, Abdulrazzaq, Shal, Mazen Abo, Alghamdi, Emad A., Alshaibani, Maged S., Zakraoui, Jezia, Mohammed, Wafaa, Gaanoun, Kamel, Elmadani, Khalid N., Ghaleb, Mustafa, Tazi, Nouamane, Alharbi, Raed, Masoud, Maraim, and Alyafeai, Zaid

Subjects
Computer Science - Computation and Language
Abstract

Masader (Alyafeai et al., 2021) created a metadata structure to be used for cataloguing Arabic NLP datasets. However, developing an easy way to explore such a catalogue is a challenging task. In order to give the optimal experience for users and researchers exploring the catalogue, several design and user experience challenges must be resolved. Furthermore, user interactions with the website may provide an easy approach to improve the catalogue. In this paper, we introduce Masader Plus, a web interface for users to browse Masader. We demonstrate data exploration, filtration, and a simple API that allows users to examine datasets from the backend. Masader Plus can be explored using this link https://arbml.github.io/masader. A video recording explaining the interface can be found here https://www.youtube.com/watch?v=SEtdlSeqchk.

Published
2022

4. Data Governance in the Age of Large-Scale Data-Driven Language Technology

Author

Jernite, Yacine, Nguyen, Huu, Biderman, Stella, Rogers, Anna, Masoud, Maraim, Danchev, Valentin, Tan, Samson, Luccioni, Alexandra Sasha, Subramani, Nishant, Dupont, Gérard, Dodge, Jesse, Lo, Kyle, Talat, Zeerak, Johnson, Isaac, Radev, Dragomir, Nikpoor, Somaieh, Frohberg, Jörg, Gokaslan, Aaron, Henderson, Peter, Bommasani, Rishi, and Mitchell, Margaret

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data. This work proposes an approach to global language data governance that attempts to organize data management amongst stakeholders, values, and rights. Our proposal is informed by prior work on distributed governance that accounts for human values and grounded by an international research collaboration that brings together researchers and practitioners from 60 countries. The framework we present is a multi-party international governance structure focused on language data, and incorporating technical and organizational tools needed to support its work., Comment: 32 pages: Full paper and Appendices; Association for Computing Machinery, New York, NY, USA, 2206-2222

Published
2022
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5. Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources

Author

McMillan-Major, Angelina, Alyafeai, Zaid, Biderman, Stella, Chen, Kimbo, De Toni, Francesco, Dupont, Gérard, Elsahar, Hady, Emezue, Chris, Aji, Alham Fikri, Ilić, Suzana, Khamis, Nurulaqilla, Leong, Colin, Masoud, Maraim, Soroa, Aitor, Suarez, Pedro Ortiz, Talat, Zeerak, van Strien, Daniel, and Jernite, Yacine

Subjects
Computer Science - Computation and Language, Computer Science - Databases
Abstract

In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models. This prioritization, however, has resulted in concerns with respect to the rights of data subjects represented in data collections, particularly when considering the difficulty in interrogating these collections due to insufficient documentation and tools for analysis. Mindful of these pitfalls, we present our methodology for a documentation-first, human-centered data collection project as part of the BigScience initiative. We identified a geographically diverse set of target language groups (Arabic, Basque, Chinese, Catalan, English, French, Indic languages, Indonesian, Niger-Congo languages, Portuguese, Spanish, and Vietnamese, as well as programming languages) for which to collect metadata on potential data sources. To structure this effort, we developed our online catalogue as a supporting tool for gathering metadata through organized public hackathons. We present our development process; analyses of the resulting resource metadata, including distributions over languages, regions, and resource types; and our lessons learned in this endeavor., Comment: 8 pages plus appendix and references

Published
2022

6. Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

Author

Alyafeai, Zaid, Masoud, Maraim, Ghaleb, Mustafa, and Al-shaibani, Maged S.

Subjects
Computer Science - Computation and Language
Abstract

The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper we create \textit{Masader}, the largest public catalogue for Arabic NLP datasets, which consists of 200 datasets annotated with 25 attributes. Furthermore, We develop a metadata annotation strategy that could be extended to other languages. We also make remarks and highlight some issues about the current status of Arabic NLP datasets and suggest recommendations to address them.

Published
2021

7. Aspects of Terminological and Named Entity Knowledge within Rule-Based Machine Translation Models for Under-Resourced Neural Machine Translation Scenarios

Author

Torregrosa, Daniel, Pasricha, Nivranshu, Masoud, Maraim, Chakravarthi, Bharathi Raja, Alonso, Juan, Casas, Noe, and Arcan, Mihael

Subjects
Computer Science - Computation and Language
Abstract

Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate text from source to target language. While this approach grants extensive control over the output of the system, the cost of formalising the needed linguistic knowledge is much higher than training a corpus-based system, where a machine learning approach is used to automatically learn to translate from examples. In this paper, we describe different approaches to leverage the information contained in rule-based machine translation systems to improve a corpus-based one, namely, a neural machine translation model, with a focus on a low-resource scenario. Three different kinds of information were used: morphological information, named entities and terminology. In addition to evaluating the general performance of the system, we systematically analysed the performance of the proposed approaches when dealing with the targeted phenomena. Our results suggest that the proposed models have limited ability to learn from external information, and most approaches do not significantly alter the results of the automatic evaluation, but our preliminary qualitative evaluation shows that in certain cases the hypothesis generated by our system exhibit favourable behaviour such as keeping the use of passive voice.

Published
2020

8. Automatic construction of knowledge graphs from text and structured data: A preliminary literature review

Author

Masoud, Maraim, Pereira, Bianca, McCrae, John, and Buitelaar, Paul

Subjects
Enterprise Knowledge Graph, Information systems → Information extraction, Knowledge Graph Construction
Abstract

Knowledge graphs have been shown to be an important data structure for many applications, including chatbot development, data integration, and semantic search. In the enterprise domain, such graphs need to be constructed based on both structured (e.g. databases) and unstructured (e.g. textual) internal data sources; preferentially using automatic approaches due to the costs associated with manual construction of knowledge graphs. However, despite the growing body of research that leverages both structured and textual data sources in the context of automatic knowledge graph construction, the research community has centered on either one type of source or the other. In this paper, we conduct a preliminary literature review to investigate approaches that can be used for the integration of textual and structured data sources in the process of automatic knowledge graph construction. We highlight the solutions currently available for use within enterprises and point areas that would benefit from further research., OASIcs, Vol. 93, 3rd Conference on Language, Data and Knowledge (LDK 2021), pages 19:1-19:9

Published
2021

9. Automatic Construction of Knowledge Graphs from Text and Structured Data: A Preliminary Literature Review

Author

Maraim Masoud and Bianca Pereira and John McCrae and Paul Buitelaar, Masoud, Maraim, Pereira, Bianca, McCrae, John, Buitelaar, Paul, Maraim Masoud and Bianca Pereira and John McCrae and Paul Buitelaar, Masoud, Maraim, Pereira, Bianca, McCrae, John, and Buitelaar, Paul

Abstract

Knowledge graphs have been shown to be an important data structure for many applications, including chatbot development, data integration, and semantic search. In the enterprise domain, such graphs need to be constructed based on both structured (e.g. databases) and unstructured (e.g. textual) internal data sources; preferentially using automatic approaches due to the costs associated with manual construction of knowledge graphs. However, despite the growing body of research that leverages both structured and textual data sources in the context of automatic knowledge graph construction, the research community has centered on either one type of source or the other. In this paper, we conduct a preliminary literature review to investigate approaches that can be used for the integration of textual and structured data sources in the process of automatic knowledge graph construction. We highlight the solutions currently available for use within enterprises and point areas that would benefit from further research.

Published
2021
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10. Back-translation approach for code-switching machine translation: A case study

Author

Masoud, Maraim, Torregrosa, Daniel, Buitelaar, Paul, Arčan, Mihael, Science Foundation Ireland, European Regional Development Fund, and Enterprise Ireland

Subjects
Code-switching, Back-translation, Machine-translation
Abstract

Recently, machine translation has demonstrated significant progress in terms of translation quality. However, most of the research has focused on translating with pure monolingual texts in the source and the target side of the parallel corpora, when in fact code-switching is very common in communication nowadays. Despite the importance of handling code-switching in the translation task, existing machine translation systems fail to accommodate the code-switching content. In this paper, we examine the phenomenon of code-switching in machine translation for low-resource languages. Through different approaches, we evaluate the performance of our systems and make some observations about the role of code-mixing in the available corpora. This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under grant agreement number SFI/12/RC/2289_P2, co-funded by the European Regional Development Fund, and the Enterprise Ireland (EI) Innovation Partnership Programme under grant number IP20180729, NURS – Neural Machine Translation for Under-Resourced Scenarios. non-peer-reviewed

Published
2019

11. Leveraging rule-based machine translation knowledge for under-resourced neural machine translation models

Author

Torregrosa, Daniel, Pasricha, Nivranshu, Chakravarth, Bharathi Raja, Masoud, Maraim, Alonso, Juan, Casas, Noe, Arcan, Mihael, Science Foundation Ireland, European Regional Development Fund, and Enterprise Ireland

Subjects
neural machine translation models, Rule-based, ComputingMilieux_MISCELLANEOUS, machine translation
Abstract

Rule-based machine translation is a machine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate from source to target language. While this approach grants total control over the output of the system, the cost of formalising the needed linguistic knowledge is much higher than training a corpus-based system, where a machine learning approach is used to automatically learn to translate from examples. In this paper, we describe different approaches to leverage the information contained in rulebased machine translation systems to improve a corpus-based one, namely, a neural machine translation model, with a focus on a low-resource scenario. Our results suggest that adding morphological information to the source language is as effective as using subword units in this particular setting. This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, co-funded by the European Regional Development Fund, and the Enterprise Ireland (EI) Innovation Partnership Programme under grant agreement No IP20180729, NURS – Neural Machine Translation for Under-Resourced Scenarios peer-reviewed 2019-08-19

Published
2019

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