Jesujoba Alabi


2021

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The Effect of Domain and Diacritics in Yoruba–English Neural Machine Translation
David Adelani | Dana Ruiter | Jesujoba Alabi | Damilola Adebonojo | Adesina Ayeni | Mofe Adeyemi | Ayodele Esther Awokoya | Cristina España-Bonet
Proceedings of Machine Translation Summit XVIII: Research Track

Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba–English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google (+8.7 BLEU) and Facebook M2M (+9.1) when translating to Yoruba and setting a high quality benchmark for future research.

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EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT
Svetlana Tchistiakova | Jesujoba Alabi | Koel Dutta Chowdhury | Sourav Dutta | Dana Ruiter
Proceedings of the Sixth Conference on Machine Translation

We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021). We submit multilingual translation models for translations to/from Icelandic (is), Norwegian-Bokmal (nb), and Swedish (sv). We employ various experimental approaches, including multilingual pre-training, back-translation, fine-tuning, and ensembling. In most translation directions, our models outperform other submitted systems.

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MasakhaNER: Named Entity Recognition for African Languages
David Ifeoluwa Adelani | Jade Abbott | Graham Neubig | Daniel D’souza | Julia Kreutzer | Constantine Lignos | Chester Palen-Michel | Happy Buzaaba | Shruti Rijhwani | Sebastian Ruder | Stephen Mayhew | Israel Abebe Azime | Shamsuddeen H. Muhammad | Chris Chinenye Emezue | Joyce Nakatumba-Nabende | Perez Ogayo | Aremu Anuoluwapo | Catherine Gitau | Derguene Mbaye | Jesujoba Alabi | Seid Muhie Yimam | Tajuddeen Rabiu Gwadabe | Ignatius Ezeani | Rubungo Andre Niyongabo | Jonathan Mukiibi | Verrah Otiende | Iroro Orife | Davis David | Samba Ngom | Tosin Adewumi | Paul Rayson | Mofetoluwa Adeyemi | Gerald Muriuki | Emmanuel Anebi | Chiamaka Chukwuneke | Nkiruka Odu | Eric Peter Wairagala | Samuel Oyerinde | Clemencia Siro | Tobius Saul Bateesa | Temilola Oloyede | Yvonne Wambui | Victor Akinode | Deborah Nabagereka | Maurice Katusiime | Ayodele Awokoya | Mouhamadane MBOUP | Dibora Gebreyohannes | Henok Tilaye | Kelechi Nwaike | Degaga Wolde | Abdoulaye Faye | Blessing Sibanda | Orevaoghene Ahia | Bonaventure F. P. Dossou | Kelechi Ogueji | Thierno Ibrahima DIOP | Abdoulaye Diallo | Adewale Akinfaderin | Tendai Marengereke | Salomey Osei
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1

2020

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Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages
Michael A. Hedderich | David Adelani | Dawei Zhu | Jesujoba Alabi | Udia Markus | Dietrich Klakow
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great improvements for many NLP tasks on a variety of languages. However, recent works also showed that results from high-resource languages could not be easily transferred to realistic, low-resource scenarios. In this work, we study trends in performance for different amounts of available resources for the three African languages Hausa, isiXhosa and on both NER and topic classification. We show that in combination with transfer learning or distant supervision, these models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. However, we also find settings where this does not hold. Our discussions and additional experiments on assumptions such as time and hardware restrictions highlight challenges and opportunities in low-resource learning.

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UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian
Sourav Dutta | Jesujoba Alabi | Saptarashmi Bandyopadhyay | Dana Ruiter | Josef van Genabith
Proceedings of the Fifth Conference on Machine Translation

This paper describes the UdS-DFKI submission to the shared task for unsupervised machine translation (MT) and very low-resource supervised MT between German (de) and Upper Sorbian (hsb) at the Fifth Conference of Machine Translation (WMT20). We submit systems for both the supervised and unsupervised tracks. Apart from various experimental approaches like bitext mining, model pre-training, and iterative back-translation, we employ a factored machine translation approach on a small BPE vocabulary.

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Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of Yorùbá and Twi
Jesujoba Alabi | Kwabena Amponsah-Kaakyire | David Adelani | Cristina España-Bonet
Proceedings of the 12th Language Resources and Evaluation Conference

The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yorùbá and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yorùbá and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yorùbá. As output of the work, we provide corpora, embeddings and the test suits for both languages.