Papers

British Creole

1. Phonology meets ideology: the meaning of orthographic practices in British Creole

   Mark Sebba

   Language problems and language planning, 1998

   bibtex

   @article{sebba1998phonology, title = {Phonology meets ideology: the meaning of orthographic practices in British Creole}, author = {Sebba, Mark}, journal = {Language problems and language planning}, volume = {22}, number = {1}, pages = {19--47}, year = {1998}, publisher = {John Benjamins}, language = {British Creole} }

Guinea Creole

1. The Gulf of Guinea Creole Corpora

   Tjerk Hagemeijer, Michel Généreux, Iris Hendrickx, Amália Mendes, Abigail Tiny, and Armando Zamora

   In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), 2014

   URL Abs bibtex

   We present the process of building linguistic corpora of the Portuguese-related Gulf of Guinea creoles, a cluster of four historically related languages: Santome, Angolar, Principense and Fa d’Ambô. We faced the typical difficulties of languages lacking an official status, such as lack of standard spelling, language variation, lack of basic language instruments, and small data sets, which comprise data from the late 19th century to the present. In order to tackle these problems, the compiled written and transcribed spoken data collected during field work trips were adapted to a normalized spelling that was applied to the four languages. For the corpus compilation we followed corpus linguistics standards. We recorded meta data for each file and added morphosyntactic information based on a part-of-speech tag set that was designed to deal with the specificities of these languages. The corpora of three of the four creoles are already available and searchable via an online web interface.

   @inproceedings{hagemeijer-etal-2014-gulf, title = {The {G}ulf of {G}uinea Creole Corpora}, author = {Hagemeijer, Tjerk and G{\'e}n{\'e}reux, Michel and Hendrickx, Iris and Mendes, Am{\'a}lia and Tiny, Abigail and Zamora, Armando}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)}, month = may, year = {2014}, address = {Reykjavik, Iceland}, publisher = {European Language Resources Association (ELRA)}, url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/438_Paper.pdf}, pages = {523--529}, language = {Guinea Creole} }

Guyanese

1. Dimensions of a Creole continuum: History, texts & linguistic analysis of Guyanese Creole

   John R Rickford

   1987

   bibtex

   @book{rickford1987dimensions, title = {Dimensions of a Creole continuum: History, texts \& linguistic analysis of Guyanese Creole}, author = {Rickford, John R}, year = {1987}, publisher = {Stanford University Press}, language = {Guyanese} }

Haitian Kreyol

1. Crowdsourced translation for emergency response in haiti: the global collaboration of local knowledge

   Robert Munro

   In In Relief 2.0 in Haiti, 2010

   bibtex

   @inproceedings{Munro10crowdsourcedtranslation, author = {Munro, Robert}, title = {Crowdsourced translation for emergency response in haiti: the global collaboration of local knowledge}, booktitle = {In Relief 2.0 in Haiti}, year = {2010}, language = {Haitian Kreyol} }

2. The Value of Monolingual Crowdsourcing in a Real-World Translation Scenario: Simulation using Haitian Creole Emergency SMS Messages

   Chang Hu, Philip Resnik, Yakov Kronrod, Vladimir Eidelman, Olivia Buzek, and Benjamin B. Bederson

   In Proceedings of the Sixth Workshop on Statistical Machine Translation, 2011

   URL bibtex

   @inproceedings{hu-etal-2011-value, title = {The Value of Monolingual Crowdsourcing in a Real-World Translation Scenario: Simulation using {H}aitian {C}reole Emergency {SMS} Messages}, author = {Hu, Chang and Resnik, Philip and Kronrod, Yakov and Eidelman, Vladimir and Buzek, Olivia and Bederson, Benjamin B.}, booktitle = {Proceedings of the Sixth Workshop on Statistical Machine Translation}, month = jul, year = {2011}, address = {Edinburgh, Scotland}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/W11-2148}, pages = {399--404}, language = {Haitian Kreyol} }

Mauritian Creole

1. Anou Tradir: Experiences In Building Statistical Machine Translation Systems For Mauritian Creole Languages – Creole, English, French

   Raj Dabre, Aneerav Sukhoo, and Pushpak Bhattacharyya

   In Proceedings of the 11th International Conference on Natural Language Processing, 2014

   URL bibtex

   @inproceedings{dabre-etal-2014-anou, title = {Anou Tradir: Experiences In Building Statistical Machine Translation Systems For Mauritian Creole Languages {--} Creole, {E}nglish, {F}rench}, author = {Dabre, Raj and Sukhoo, Aneerav and Bhattacharyya, Pushpak}, booktitle = {Proceedings of the 11th International Conference on Natural Language Processing}, month = dec, year = {2014}, address = {Goa, India}, publisher = {NLP Association of India}, url = {https://aclanthology.org/W14-5113}, pages = {82--88}, language = {Mauritian Creole} }

2. The making of Mauritian Creole Creole. Analyses diachroniques à partir des textes anciens

   Philip Baker, and Guillaume Fon Sing

   2007

   bibtex

   @book{baker2007making, title = {The making of Mauritian Creole Creole. Analyses diachroniques {\`a} partir des textes anciens}, author = {Baker, Philip and Sing, Guillaume Fon}, number = {9}, year = {2007}, publisher = {Battlebridge}, language = {Mauritian Creole} }

3. KreolMorisienMT: A Dataset for Mauritian Creole Machine Translation

   Raj Dabre, and Aneerav Sukhoo

   In Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022, 2022

   URL Abs bibtex

   In this paper, we describe KreolMorisienMT, a dataset for benchmarking machine translation quality of Mauritian Creole. Mauritian Creole (Kreol Morisien) is a French-based creole and a lingua franca of the Republic of Mauritius. KreolMorisienMT consists of a parallel corpus between English and Kreol Morisien, French and Kreol Morisien and a monolingual corpus for Kreol Morisien. We first give an overview of Kreol Morisien and then describe the steps taken to create the corpora. Thereafter, we benchmark Kreol Morisien ↔ English and Kreol Morisien ↔ French models leveraging pre-trained models and multilingual transfer learning. Human evaluation reveals our systems’ high translation quality.

   @inproceedings{dabre-sukhoo-2022-kreolmorisienmt, title = {{K}reol{M}orisien{MT}: A Dataset for Mauritian Creole Machine Translation}, author = {Dabre, Raj and Sukhoo, Aneerav}, editor = {He, Yulan and Ji, Heng and Li, Sujian and Liu, Yang and Chang, Chua-Hui}, booktitle = {Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022}, month = nov, year = {2022}, address = {Online only}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.findings-aacl.3}, pages = {22--29}, language = {Mauritian Creole} }

4. Automatic Speech Recognition and Query By Example for Creole Languages Documentation

   Cécile Macaire, Didier Schwab, Benjamin Lecouteux, and Emmanuel Schang

   In Findings of the Association for Computational Linguistics: ACL 2022, 2022

   URL Abs bibtex

   We investigate the exploitation of self-supervised models for two Creole languages with few resources: Gwadloupéyen and Morisien. Automatic language processing tools are almost non-existent for these two languages. We propose to use about one hour of annotated data to design an automatic speech recognition system for each language. We evaluate how much data is needed to obtain a query-by-example system that is usable by linguists. Moreover, our experiments show that multilingual self-supervised models are not necessarily the most efficient for Creole languages.

   @inproceedings{macaire-etal-2022-automatic, title = {Automatic Speech Recognition and Query By Example for Creole Languages Documentation}, author = {Macaire, C{\'e}cile and Schwab, Didier and Lecouteux, Benjamin and Schang, Emmanuel}, editor = {Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2022}, month = may, year = {2022}, address = {Dublin, Ireland}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.findings-acl.197}, doi = {10.18653/v1/2022.findings-acl.197}, pages = {2512--2520}, language = {Mauritian Creole} }

Nigerian Pidgin

1. Nigerian PidginNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages

   Wuraola Fisayo Oyewusi, Olubayo Adekanmbi, Ife Okoh, Vitus Onuigwe, Mary Idera Salami, Opeyemi Osakuade, Sharon Ibejih, and Usman Abdullahi Musa

   ArXiv, 2021

   bibtex

   @article{Oyewusi2021Nigerian PidginNERC, title = {Nigerian PidginNER : Comprehensive Named Entity Recognition for 5 Nigerian Languages}, author = {Oyewusi, Wuraola Fisayo and Adekanmbi, Olubayo and Okoh, Ife and Onuigwe, Vitus and Salami, Mary Idera and Osakuade, Opeyemi and Ibejih, Sharon and Musa, Usman Abdullahi}, journal = {ArXiv}, year = {2021}, volume = {abs/2105.00810}, language = {Nigerian Pidgin} }

2. Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin

   Daniel Ajisafe, Oluwabukola Grace Adegboro, Esther Oduntan, and Tayo Oladiran Arulogun

   ArXiv, 2020

   bibtex

   @article{Ajisafe2020TowardsET, title = {Towards End-to-End Training of Automatic Speech Recognition for Nigerian Pidgin}, author = {Ajisafe, Daniel and Adegboro, Oluwabukola Grace and Oduntan, Esther and Arulogun, Tayo Oladiran}, journal = {ArXiv}, year = {2020}, volume = {abs/2010.11123}, language = {Nigerian Pidgin} }

3. Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification

   Wuraola Fisayo Oyewusi, Olubayo Adekanmbi, and Olalekan Akinsande

   ArXiv, 2020

   bibtex

   @article{Oyewusi2020SemanticEO, title = {Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification}, author = {Oyewusi, Wuraola Fisayo and Adekanmbi, Olubayo and Akinsande, Olalekan}, journal = {ArXiv}, year = {2020}, volume = {abs/2003.12450}, language = {Nigerian Pidgin} }

4. Developing Resources for Automated Speech Processing of the African Language Nigerian Pidgin (Nigerian Pidgin)

   B. Bigi, B. Caron, and Oyelere S. Abiola

   In , 2017

   bibtex

   @inproceedings{Bigi2017DevelopingRF, title = {Developing Resources for Automated Speech Processing of the African Language Nigerian Pidgin (Nigerian Pidgin)}, author = {Bigi, B. and Caron, B. and Abiola, Oyelere S.}, year = {2017}, language = {Nigerian Pidgin} }

5. Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin

   Orevaoghene Ahia, and Kelechi Ogueji

   ArXiv, 2020

   bibtex

   @article{Ahia2020TowardsSA, title = {Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin}, author = {Ahia, Orevaoghene and Ogueji, Kelechi}, journal = {ArXiv}, year = {2020}, volume = {abs/2003.12660}, language = {Nigerian Pidgin} }

6. Wétin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior, in Nigerian Online Discussions

   Innocent Ndubuisi-Obi, Sayan Ghosh, and David Jurgens

   In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2019

   bibtex

   @inproceedings{obighosh2019naija, title = {Wétin dey with these comments? Modeling Sociolinguistic Factors Affecting Code-switching Behavior, in Nigerian Online Discussions}, author = {Ndubuisi-Obi, Innocent and Ghosh, Sayan and Jurgens, David}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, year = {2019}, language = {Nigerian Pidgin} }

7. A Surface-Syntactic UD Treebank for Naija

   Bernard Caron, Marine Courtin, Kim Gerdes, and Sylvain Kahane

   In Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019), 2019

   URL bibtex

   @inproceedings{caron-etal-2019-surface, title = {A Surface-Syntactic {UD} Treebank for {N}aija}, author = {Caron, Bernard and Courtin, Marine and Gerdes, Kim and Kahane, Sylvain}, booktitle = {Proceedings of the 18th International Workshop on Treebanks and Linguistic Theories (TLT, SyntaxFest 2019)}, month = aug, year = {2019}, address = {Paris, France}, publisher = {Association for Computational Linguistics}, url = {https://www.aclweb.org/anthology/W19-7803}, doi = {10.18653/v1/W19-7803}, pages = {13--24}, language = {Nigerian Pidgin} }

8. Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification

   Wuraola Fisayo Oyewusi, Olubayo Adekanmbi, and O. Akinsande

   ArXiv, 2020

   bibtex

   @article{Oyewusi2020SemanticEP, title = {Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification}, author = {Oyewusi, Wuraola Fisayo and Adekanmbi, Olubayo and Akinsande, O.}, journal = {ArXiv}, year = {2020}, volume = {abs/2003.12450}, language = {Nigerian Pidgin} }

9. Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning

   Jesujoba O. Alabi, David Ifeoluwa Adelani, Marius Mosbach, and Dietrich Klakow

   In Proceedings of the 29th International Conference on Computational Linguistics, 2022

   URL Abs bibtex

   Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is language adaptive fine-tuning (LAFT) — fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to target language individually takes large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform multilingual adaptive fine-tuning on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50%. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.

   @inproceedings{alabi-etal-2022-adapting, title = {Adapting Pre-trained Language Models to {A}frican Languages via Multilingual Adaptive Fine-Tuning}, author = {Alabi, Jesujoba O. and Adelani, David Ifeoluwa and Mosbach, Marius and Klakow, Dietrich}, editor = {Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}, booktitle = {Proceedings of the 29th International Conference on Computational Linguistics}, month = oct, year = {2022}, address = {Gyeongju, Republic of Korea}, publisher = {International Committee on Computational Linguistics}, url = {https://aclanthology.org/2022.coling-1.382}, pages = {4336--4349}, language = {Nigerian Pidgin} }

10. Transfer Learning for Code-Mixed Data: Do Pretraining Languages Matter?

    Kushal Tatariya, Heather Lent, and Miryam Lhoneux

    In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, 2023

    URL Abs bibtex

    Monolinguals make up a minority of the world’s speakers, and yet most language technologies lag behind in handling linguistic behaviours produced by bilingual and multilingual speakers. A commonly observed phenomenon in such communities is code-mixing, which is prevalent on social media, and thus requires attention in NLP research. In this work, we look into the ability of pretrained language models to handle code-mixed data, with a focus on the impact of languages present in pretraining on the downstream performance of the model as measured on the task of sentiment analysis. Ultimately, we find that the pretraining language has little effect on performance when the model sees code-mixed data during downstream finetuning. We also evaluate the models on code-mixed data in a zero-shot setting, after task-specific finetuning on a monolingual dataset. We find that this brings out differences in model performance that can be attributed to the pretraining languages. We present a thorough analysis of these findings that also looks at model performance based on the composition of participating languages in the code-mixed datasets.

    @inproceedings{tatariya-etal-2023-transfer, title = {Transfer Learning for Code-Mixed Data: Do Pretraining Languages Matter?}, author = {Tatariya, Kushal and Lent, Heather and de Lhoneux, Miryam}, editor = {Barnes, Jeremy and De Clercq, Orph{\'e}e and Klinger, Roman}, booktitle = {Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis}, month = jul, year = {2023}, address = {Toronto, Canada}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2023.wassa-1.32}, doi = {10.18653/v1/2023.wassa-1.32}, pages = {365--378}, language = {Nigerian Pidgin} }

11. Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages

    David Ifeoluwa Adelani, A Seza Doğruöz, Iyanuoluwa Shode, and Anuoluwapo Aremu

    arXiv preprint arXiv:2404.19442, 2024

    bibtex

    @article{adelani2024nigerian, title = {Which Nigerian-Pidgin does Generative AI speak?: Issues about Representativeness and Bias for Multilingual and Low Resource Languages}, author = {Adelani, David Ifeoluwa and Do{\u{g}}ru{\"o}z, A Seza and Shode, Iyanuoluwa and Aremu, Anuoluwapo}, journal = {arXiv preprint arXiv:2404.19442}, year = {2024}, language = {Nigerian Pidgin} }

Singlish

1. Developing a concept-level knowledge base for sentiment analysis in Singlish

   Rajiv Bajpai, Soujanya Poria, Danyuan Ho, and Erik Cambria

   CoRR, 2017

   URL bibtex

   @article{DBLP:journals/corr/BajpaiPHC17, author = {Bajpai, Rajiv and Poria, Soujanya and Ho, Danyuan and Cambria, Erik}, title = {Developing a concept-level knowledge base for sentiment analysis in Singlish}, journal = {CoRR}, volume = {abs/1707.04408}, year = {2017}, url = {http://arxiv.org/abs/1707.04408}, eprinttype = {arXiv}, eprint = {1707.04408}, timestamp = {Mon, 08 Mar 2021 16:26:41 +0100}, biburl = {https://dblp.org/rec/journals/corr/BajpaiPHC17.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, language = {Singlish} }

2. Developing a concept-level knowledge base for sentiment analysis in Singlish

   Rajiv Bajpai, Soujanya Poria, Danyuan Ho, and Erik Cambria

   CoRR, 2017

   URL bibtex

   @article{DBLP:journals/corr/BajpaiPHC18, author = {Bajpai, Rajiv and Poria, Soujanya and Ho, Danyuan and Cambria, Erik}, title = {Developing a concept-level knowledge base for sentiment analysis in Singlish}, journal = {CoRR}, volume = {abs/1707.04408}, year = {2017}, url = {http://arxiv.org/abs/1707.04408}, eprinttype = {arXiv}, eprint = {1707.04408}, timestamp = {Mon, 08 Mar 2021 16:26:41 +0100}, biburl = {https://dblp.org/rec/journals/corr/BajpaiPHC17.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}, language = {Singlish} }

3. Universal Dependencies Parsing for Colloquial Singaporean English

   Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, Jie Yang, and Hai Leong Chieu

   In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017

   URL Abs bibtex

   Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.

   @inproceedings{wang-etal-2017-universal, title = {{U}niversal {D}ependencies Parsing for Colloquial Singaporean {E}nglish}, author = {Wang, Hongmin and Zhang, Yue and Chan, GuangYong Leonard and Yang, Jie and Chieu, Hai Leong}, booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, month = jul, year = {2017}, address = {Vancouver, Canada}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/P17-1159}, doi = {10.18653/v1/P17-1159}, pages = {1732--1744}, language = {Singlish} }

4. Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization

   Zhengyuan Liu, Shikang Ni, Ai Ti Aw, and Nancy F. Chen

   In Proceedings of the 29th International Conference on Computational Linguistics, 2022

   URL Abs bibtex

   Within the natural language processing community, English is by far the most resource-rich language. There is emerging interest in conducting translation via computational approaches to conform its dialects or creole languages back to standard English. This computational approach paves the way to leverage generic English language backbones, which are beneficial for Many downstream tasks. However, in practical online communication scenarios, the use of language varieties is often accompanied by noisy user-generated content, making this translation task more challenging. In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects. We formulate the task in three different linguistic dimensions: lexical level normalization, syntactic level editing, and semantic level rewriting. We build an annotated dataset of Singlish-to-Standard English messages, and report performance on a perturbation-resilient sequence-to-sequence model. Experimental results show that the model produces reasonable generation results, and can improve the performance of downstream tasks like stance detection.

   @inproceedings{liu-etal-2022-singlish, title = {{S}inglish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization}, author = {Liu, Zhengyuan and Ni, Shikang and Aw, Ai Ti and Chen, Nancy F.}, editor = {Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}, booktitle = {Proceedings of the 29th International Conference on Computational Linguistics}, month = oct, year = {2022}, address = {Gyeongju, Republic of Korea}, publisher = {International Committee on Computational Linguistics}, url = {https://aclanthology.org/2022.coling-1.345}, pages = {3924--3936}, language = {Singlish} }

5. Singlish Where Got Rules One? Constructing a Computational Grammar for Singlish

   Siew Yeng Chow, and Francis Bond

   In Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

   URL Abs bibtex

   Singlish is a variety of English spoken in Singapore. In this paper, we share some of its grammar features and how they are implemented in the construction of a computational grammar of Singlish as a branch of English grammar. New rules were created and existing ones from standard English grammar of the English Resource Grammar (ERG) were changed in this branch to cater to how Singlish works. In addition, Singlish lexicon was added into the grammar together with some new lexical types. We used Head-driven Phrase Structure Grammar (HPSG) as the framework for this project of a creating a working computational grammar. As part of building the language resource, we also collected and formatted some data from the internet as part of a test suite for Singlish. Finally, the computational grammar was tested against a set of gold standard trees and compared with the standard English grammar to find out how well the grammar fares in analysing Singlish.

   @inproceedings{chow-bond-2022-singlish, title = {{S}inglish Where Got Rules One? Constructing a Computational Grammar for {S}inglish}, author = {Chow, Siew Yeng and Bond, Francis}, editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios}, booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference}, month = jun, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, url = {https://aclanthology.org/2022.lrec-1.562}, pages = {5243--5250}, language = {Singlish} }

6. Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages

   Zheng Xin Yong, Ruochen Zhang, Jessica Forde, Skyler Wang, Arjun Subramonian, Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Lintang Sutawika, Jan Christian Blaise Cruz, Yin Lin Tan, Long Phan, Long Phan, Rowena Garcia, Thamar Solorio, and Alham Aji

   In Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching, 2023

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   While code-mixing is a common linguistic practice in many parts of the world, collecting high-quality and low-cost code-mixed data remains a challenge for natural language processing (NLP) research. The recent proliferation of Large Language Models (LLMs) compels one to ask: how capable are these systems in generating code-mixed data? In this paper, we explore prompting multilingual LLMs in a zero-shot manner to generate code-mixed data for seven languages in South East Asia (SEA), namely Indonesian, Malay, Chinese, Tagalog, Vietnamese, Tamil, and Singlish. We find that publicly available multilingual instruction-tuned models such as BLOOMZ and Flan-T5-XXL are incapable of producing texts with phrases or clauses from different languages. ChatGPT exhibits inconsistent capabilities in generating code-mixed texts, wherein its performance varies depending on the prompt template and language pairing. For instance, ChatGPT generates fluent and natural Singlish texts (an English-based creole spoken in Singapore), but for English-Tamil language pair, the system mostly produces grammatically incorrect or semantically meaningless utterances. Furthermore, it may erroneously introduce languages not specified in the prompt. Based on our investigation, existing multilingual LLMs exhibit a wide range of proficiency in code-mixed data generation for SEA languages. As such, we advise against using LLMs in this context without extensive human checks.

   @inproceedings{yong-etal-2023-prompting, title = {Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South {E}ast {A}sian Languages}, author = {Yong, Zheng Xin and Zhang, Ruochen and Forde, Jessica and Wang, Skyler and Subramonian, Arjun and Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta and Sutawika, Lintang and Cruz, Jan Christian Blaise and Tan, Yin Lin and Phan, Long and Phan, Long and Garcia, Rowena and Solorio, Thamar and Aji, Alham}, editor = {Winata, Genta and Kar, Sudipta and Zhukova, Marina and Solorio, Thamar and Diab, Mona and Sitaram, Sunayana and Choudhury, Monojit and Bali, Kalika}, booktitle = {Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching}, month = dec, year = {2023}, address = {Singapore}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2023.calcs-1.5}, doi = {10.18653/v1/2023.calcs-1.5}, pages = {43--63}, language = {Singlish} }

West African Pidgin

1. PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English

   Kelechi Ogueji, and Orevaoghene Ahia

   ArXiv, 2019

   bibtex

   @article{Ogueji2019PidginUNMTUN, title = {PidginUNMT: Unsupervised Neural Machine Translation from West African Pidgin to English}, author = {Ogueji, Kelechi and Ahia, Orevaoghene}, journal = {ArXiv}, year = {2019}, volume = {abs/1912.03444}, language = {West African Pidgin} }

Jamaican Creole English

1. JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset

   Ruth-Ann Armstrong, John Hewitt, and Christopher Manning

   In Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

   URL bibtex

   @inproceedings{armstrong-etal-2022-jampatoisnli, title = {{J}am{P}atois{NLI}: A {J}amaican {P}atois Natural Language Inference Dataset}, author = {Armstrong, Ruth-Ann and Hewitt, John and Manning, Christopher}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2022}, month = dec, year = {2022}, address = {Abu Dhabi, United Arab Emirates}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.findings-emnlp.389}, pages = {5307--5320}, language = {Jamaican Creole English} }

Guadeloupean Creole

1. Automatic Speech Recognition and Query By Example for Creole Languages Documentation

   Cécile Macaire, Didier Schwab, Benjamin Lecouteux, and Emmanuel Schang

   In Findings of the Association for Computational Linguistics: ACL 2022, 2022

   URL Abs bibtex

   We investigate the exploitation of self-supervised models for two Creole languages with few resources: Gwadloupéyen and Morisien. Automatic language processing tools are almost non-existent for these two languages. We propose to use about one hour of annotated data to design an automatic speech recognition system for each language. We evaluate how much data is needed to obtain a query-by-example system that is usable by linguists. Moreover, our experiments show that multilingual self-supervised models are not necessarily the most efficient for Creole languages.

   @inproceedings{macaire-etal-2022-automatid, title = {Automatic Speech Recognition and Query By Example for Creole Languages Documentation}, author = {Macaire, C{\'e}cile and Schwab, Didier and Lecouteux, Benjamin and Schang, Emmanuel}, editor = {Muresan, Smaranda and Nakov, Preslav and Villavicencio, Aline}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2022}, month = may, year = {2022}, address = {Dublin, Ireland}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.findings-acl.197}, doi = {10.18653/v1/2022.findings-acl.197}, pages = {2512--2520}, language = {Guadeloupean Creole} }

Antillean Creole

1. How to Parse a Creole: When Martinican Creole Meets French

   Ludovic Mompelat, Daniel Dakota, and Sandra Kübler

   In Proceedings of the 29th International Conference on Computational Linguistics, 2022

   URL Abs bibtex

   We investigate methods to develop a parser for Martinican Creole, a highly under-resourced language, using a French treebank. We compare transfer learning and multi-task learning models and examine different input features and strategies to handle the massive size imbalance between the treebanks. Surprisingly, we find that a simple concatenated (French + Martinican Creole) baseline yields optimal results even though it has access to only 80 Martinican Creole sentences. POS embeddings work better than lexical ones, but they suffer from negative transfer.

   @inproceedings{mompelat-etal-2022-parse, title = {How to Parse a Creole: When Martinican Creole Meets {F}rench}, author = {Mompelat, Ludovic and Dakota, Daniel and K{\"u}bler, Sandra}, editor = {Calzolari, Nicoletta and Huang, Chu-Ren and Kim, Hansaem and Pustejovsky, James and Wanner, Leo and Choi, Key-Sun and Ryu, Pum-Mo and Chen, Hsin-Hsi and Donatelli, Lucia and Ji, Heng and Kurohashi, Sadao and Paggio, Patrizia and Xue, Nianwen and Kim, Seokhwan and Hahm, Younggyun and He, Zhong and Lee, Tony Kyungil and Santus, Enrico and Bond, Francis and Na, Seung-Hoon}, booktitle = {Proceedings of the 29th International Conference on Computational Linguistics}, month = oct, year = {2022}, address = {Gyeongju, Republic of Korea}, publisher = {International Committee on Computational Linguistics}, url = {https://aclanthology.org/2022.coling-1.387}, pages = {4397--4406}, language = {Antillean Creole} }

*Many

1. AfroLID: A Neural Language Identification Tool for African Languages

   Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed, and Alcides Inciarte

   In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

   URL Abs bibtex

   Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world’s 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for 517 African languages and varieties. AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems. When evaluated on our blind Test set, AfroLID achieves 95.89 F_1-score. We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages. We further show the utility of AfroLID in the wild by testing it on the acutely under-served Twitter domain. Finally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both showcase AfroLID’s powerful capabilities and limitations

   @inproceedings{adebara-etal-2022-afrolid, title = {{A}fro{LID}: A Neural Language Identification Tool for {A}frican Languages}, author = {Adebara, Ife and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad and Inciarte, Alcides}, editor = {Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue}, booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing}, month = dec, year = {2022}, address = {Abu Dhabi, United Arab Emirates}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.emnlp-main.128}, doi = {10.18653/v1/2022.emnlp-main.128}, pages = {1958--1981}, language = {*Many} }

2. Explorations in creole research with phylogenetic tools

   Aymeric Daval-Markussen, and Peter Bakker

   In Proceedings of the EACL 2012 Joint Workshop of LINGVIS & UNCLH, 2012

   URL bibtex

   @inproceedings{daval-markussen-bakker-2012-explorations, title = {Explorations in creole research with phylogenetic tools}, author = {Daval-Markussen, Aymeric and Bakker, Peter}, editor = {Butt, Miriam and Carpendale, Sheelagh and Penn, Gerald and Proki{\'c}, Jelena and Cysouw, Michael}, booktitle = {Proceedings of the {EACL} 2012 Joint Workshop of {LINGVIS} {\&} {UNCLH}}, month = apr, year = {2012}, address = {Avignon, France}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/W12-0213}, pages = {89--97}, language = {*Many} }

3. Statistical Modeling of Creole Genesis

   Yugo Murawaki

   In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016

   URL bibtex

   @inproceedings{murawaki-2016-statistical, title = {Statistical Modeling of Creole Genesis}, author = {Murawaki, Yugo}, editor = {Knight, Kevin and Nenkova, Ani and Rambow, Owen}, booktitle = {Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies}, month = jun, year = {2016}, address = {San Diego, California}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/N16-1158}, doi = {10.18653/v1/N16-1158}, pages = {1329--1339}, language = {*Many} }

4. On Language Models for Creoles

   Heather Lent, Emanuele Bugliarello, Miryam Lhoneux, Chen Qiu, and Anders Søgaard

   In Proceedings of the 25th Conference on Computational Natural Language Learning, 2021

   URL Abs bibtex

   Creole languages such as Nigerian Pidgin English and Haitian Creole are under-resourced and largely ignored in the NLP literature. Creoles typically result from the fusion of a foreign language with multiple local languages, and what grammatical and lexical features are transferred to the creole is a complex process. While creoles are generally stable, the prominence of some features may be much stronger with certain demographics or in some linguistic situations. This paper makes several contributions: We collect existing corpora and release models for Haitian Creole, Nigerian Pidgin English, and Singaporean Colloquial English. We evaluate these models on intrinsic and extrinsic tasks. Motivated by the above literature, we compare standard language models with distributionally robust ones and find that, somewhat surprisingly, the standard language models are superior to the distributionally robust ones. We investigate whether this is an effect of over-parameterization or relative distributional stability, and find that the difference persists in the absence of over-parameterization, and that drift is limited, confirming the relative stability of creole languages.

   @inproceedings{lent-etal-2021-language, title = {On Language Models for Creoles}, author = {Lent, Heather and Bugliarello, Emanuele and de Lhoneux, Miryam and Qiu, Chen and S{\o}gaard, Anders}, editor = {Bisazza, Arianna and Abend, Omri}, booktitle = {Proceedings of the 25th Conference on Computational Natural Language Learning}, month = nov, year = {2021}, address = {Online}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2021.conll-1.5}, doi = {10.18653/v1/2021.conll-1.5}, pages = {58--71}, language = {*Many} }

5. Ancestor-to-Creole Transfer is Not a Walk in the Park

   Heather Lent, Emanuele Bugliarello, and Anders Søgaard

   In Proceedings of the Third Workshop on Insights from Negative Results in NLP, 2022

   URL Abs bibtex

   We aim to learn language models for Creole languages for which large volumes of data are not readily available, and therefore explore the potential transfer from ancestor languages (the ‘Ancestry Transfer Hypothesis’). We find that standard transfer methods do not facilitate ancestry transfer. Surprisingly, different from other non-Creole languages, a very distinct two-phase pattern emerges for Creoles: As our training losses plateau, and language models begin to overfit on their source languages, perplexity on the Creoles drop. We explore if this compression phase can lead to practically useful language models (the ‘Ancestry Bottleneck Hypothesis’), but also falsify this. Moreover, we show that Creoles even exhibit this two-phase pattern even when training on random, unrelated languages. Thus Creoles seem to be typological outliers and we speculate whether there is a link between the two observations.

   @inproceedings{lent-etal-2022-ancestor, title = {Ancestor-to-Creole Transfer is Not a Walk in the Park}, author = {Lent, Heather and Bugliarello, Emanuele and S{\o}gaard, Anders}, editor = {Tafreshi, Shabnam and Sedoc, Jo{\~a}o and Rogers, Anna and Drozd, Aleksandr and Rumshisky, Anna and Akula, Arjun}, booktitle = {Proceedings of the Third Workshop on Insights from Negative Results in NLP}, month = may, year = {2022}, address = {Dublin, Ireland}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2022.insights-1.9}, doi = {10.18653/v1/2022.insights-1.9}, pages = {68--74}, language = {*Many} }

6. What a Creole Wants, What a Creole Needs

   Heather Lent, Kelechi Ogueji, Miryam Lhoneux, Orevaoghene Ahia, and Anders Søgaard

   In Proceedings of the Thirteenth Language Resources and Evaluation Conference, 2022

   URL Abs bibtex

   In recent years, the natural language processing (NLP) community has given increased attention to the disparity of efforts directed towards high-resource languages over low-resource ones. Efforts to remedy this delta often begin with translations of existing English datasets into other languages. However, this approach ignores that different language communities have different needs. We consider a group of low-resource languages, creole languages. Creoles are both largely absent from the NLP literature, and also often ignored by society at large due to stigma, despite these languages having sizable and vibrant communities. We demonstrate, through conversations with creole experts and surveys of creole-speaking communities, how the things needed from language technology can change dramatically from one language to another, even when the languages are considered to be very similar to each other, as with creoles. We discuss the prominent themes arising from these conversations, and ultimately demonstrate that useful language technology cannot be built without involving the relevant community.

   @inproceedings{lent-etal-2022-creole, title = {What a Creole Wants, What a Creole Needs}, author = {Lent, Heather and Ogueji, Kelechi and de Lhoneux, Miryam and Ahia, Orevaoghene and S{\o}gaard, Anders}, editor = {Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios}, booktitle = {Proceedings of the Thirteenth Language Resources and Evaluation Conference}, month = jun, year = {2022}, address = {Marseille, France}, publisher = {European Language Resources Association}, url = {https://aclanthology.org/2022.lrec-1.691}, pages = {6439--6449}, language = {*Many} }

7. Kreyol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages

   Nathaniel R Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Bizon Monroc, Loı̈c Grobol, Hasan Muhammad, Ashi Garg, Naome A Etori, and others

   arXiv preprint arXiv:2405.05376, 2024

   bibtex

   @article{robinson2024krey, title = {Kreyol-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages}, author = {Robinson, Nathaniel R and Dabre, Raj and Shurtz, Ammon and Dent, Rasul and Onesi, Onenamiyi and Monroc, Claire Bizon and Grobol, Lo{\"\i}c and Muhammad, Hasan and Garg, Ashi and Etori, Naome A and others}, journal = {arXiv preprint arXiv:2405.05376}, year = {2024}, language = {*Many} }

8. CreoleVal: Multilingual Multitask Benchmarks for Creoles

   Heather Lent, Kushal Tatariya, Raj Dabre, Yiyi Chen, Marcell Fekete, Esther Ploeger, Li Zhou, Ruth-Ann Armstrong, Abee Eijansantos, Catriona Malau, Hans Erik Heje, Ernests Lavrinovics, Diptesh Kanojia, Paul Belony, Marcel Bollmann, Loïc Grobol, Miryam Lhoneux, Daniel Hershcovich, Michel DeGraff, Anders Søgaard, and Johannes Bjerva

   2024
9. SERENGETI: Massively Multilingual Language Models for Africa

   Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed, and Alcides Alcoba Inciarte

   In Findings of the Association for Computational Linguistics: ACL 2023, 2023

   URL Abs bibtex

   Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. To date, only ~31 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks, achieving 82.27 average F_1. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research. Anonymous link

   @inproceedings{adebara-etal-2023-serengeti, title = {{SERENGETI}: Massively Multilingual Language Models for {A}frica}, author = {Adebara, Ife and Elmadany, AbdelRahim and Abdul-Mageed, Muhammad and Alcoba Inciarte, Alcides}, editor = {Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki}, booktitle = {Findings of the Association for Computational Linguistics: ACL 2023}, month = jul, year = {2023}, address = {Toronto, Canada}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2023.findings-acl.97}, doi = {10.18653/v1/2023.findings-acl.97}, pages = {1498--1537}, language = {*Many} }