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  1. AU="Dossou, Bonaventure F. P."
  2. AU="Wang, S Z"
  3. AU="Andreko, Susan K"
  4. AU="Ames, DeWayne"
  5. AU="Fokom Domgue, Joel"
  6. AU="Soubani, Ayman O"
  7. AU="Weir, Andrew"
  8. AU="McGowan, Alessia"
  9. AU=Hoepler Wolfgang AU=Hoepler Wolfgang
  10. AU="Pintér, Nándor K"
  11. AU=Linask Kersti K
  12. AU="Arya, Akanksha"
  13. AU="Jue, Nathaniel"
  14. AU="Favaro, Enrica"
  15. AU="Santana, Margarida M"
  16. AU="Wiegand, Ryan E"
  17. AU="Cosio, Daniela S"
  18. AU="Yasuda, Michiyuki"
  19. AU="Theodoratou, Evropi"
  20. AU="Ernfors, Patrik"
  21. AU="Pingel, Simon"
  22. AU="W. T. Lawrence"
  23. AU="Tietzmann, Marcel"
  24. AU="DeRenzo, Christopher"

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  1. Buch ; Online: A Study of Acquisition Functions for Medical Imaging Deep Active Learning

    Dossou, Bonaventure F. P.

    2024  

    Abstract: The Deep Learning revolution has enabled groundbreaking achievements in recent years. From breast cancer detection to protein folding, deep learning algorithms have been at the core of very important advancements. However, these modern advancements are ... ...

    Abstract The Deep Learning revolution has enabled groundbreaking achievements in recent years. From breast cancer detection to protein folding, deep learning algorithms have been at the core of very important advancements. However, these modern advancements are becoming more and more data-hungry, especially on labeled data whose availability is scarce: this is even more prevalent in the medical context. In this work, we show how active learning could be very effective in data scarcity situations, where obtaining labeled data (or annotation budget is very limited). We compare several selection criteria (BALD, MeanSTD, and MaxEntropy) on the ISIC 2016 dataset. We also explored the effect of acquired pool size on the model's performance. Our results suggest that uncertainty is useful to the Melanoma detection task, and confirms the hypotheses of the author of the paper of interest, that \textit{bald} performs on average better than other acquisition functions. Our extended analyses however revealed that all acquisition functions perform badly on the positive (cancerous) samples, suggesting exploitation of class unbalance, which could be crucial in real-world settings. We finish by suggesting future work directions that would be useful to improve this current work. The code of our implementation is open-sourced at \url{https://github.com/bonaventuredossou/ece526_course_project}

    Comment: Best Poster Award at Deep Learning Indaba 2023 Conference
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence ; Computer Science - Human-Computer Interaction ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2024-01-28
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  2. Buch ; Online: AfriVEC

    Dossou, Bonaventure F. P. / Sabry, Mohammed

    Word Embedding Models for African Languages. Case Study of Fon and Nobiin

    2021  

    Abstract: From Word2Vec to GloVe, word embedding models have played key roles in the current state-of-the-art results achieved in Natural Language Processing. Designed to give significant and unique vectorized representations of words and entities, those models ... ...

    Abstract From Word2Vec to GloVe, word embedding models have played key roles in the current state-of-the-art results achieved in Natural Language Processing. Designed to give significant and unique vectorized representations of words and entities, those models have proven to efficiently extract similarities and establish relationships reflecting semantic and contextual meaning among words and entities. African Languages, representing more than 31% of the worldwide spoken languages, have recently been subject to lots of research. However, to the best of our knowledge, there are currently very few to none word embedding models for those languages words and entities, and none for the languages under study in this paper. After describing Glove, Word2Vec, and Poincar\'e embeddings functionalities, we build Word2Vec and Poincar\'e word embedding models for Fon and Nobiin, which show promising results. We test the applicability of transfer learning between these models as a landmark for African Languages to jointly involve in mitigating the scarcity of their resources, and attempt to provide linguistic and social interpretations of our results. Our main contribution is to arouse more interest in creating word embedding models proper to African Languages, ready for use, and that can significantly improve the performances of Natural Language Processing downstream tasks on them. The official repository and implementation is at https://github.com/bonaventuredossou/afrivec
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2021-03-08
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    Dokumenttyp Buch ; Online
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  3. Buch ; Online: MMTAfrica

    Emezue, Chris C. / Dossou, Bonaventure F. P.

    Multilingual Machine Translation for African Languages

    2022  

    Abstract: In this paper, we focus on the task of multilingual machine translation for African languages and describe our contribution in the 2021 WMT Shared Task: Large-Scale Multilingual Machine Translation. We introduce MMTAfrica, the first many-to-many ... ...

    Abstract In this paper, we focus on the task of multilingual machine translation for African languages and describe our contribution in the 2021 WMT Shared Task: Large-Scale Multilingual Machine Translation. We introduce MMTAfrica, the first many-to-many multilingual translation system for six African languages: Fon (fon), Igbo (ibo), Kinyarwanda (kin), Swahili/Kiswahili (swa), Xhosa (xho), and Yoruba (yor) and two non-African languages: English (eng) and French (fra). For multilingual translation concerning African languages, we introduce a novel backtranslation and reconstruction objective, BT\&REC, inspired by the random online back translation and T5 modeling framework respectively, to effectively leverage monolingual data. Additionally, we report improvements from MMTAfrica over the FLORES 101 benchmarks (spBLEU gains ranging from $+0.58$ in Swahili to French to $+19.46$ in French to Xhosa). We release our dataset and code source at https://github.com/edaiofficial/mmtafrica.

    Comment: WMT Shared Task, EMNLP 2021 (version 2)
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2022-04-08
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  4. Buch ; Online: FSER

    Dossou, Bonaventure F. P. / Gbenou, Yeno K. S.

    Deep Convolutional Neural Networks for Speech Emotion Recognition

    2021  

    Abstract: Using mel-spectrograms over conventional MFCCs features, we assess the abilities of convolutional neural networks to accurately recognize and classify emotions from speech data. We introduce FSER, a speech emotion recognition model trained on four valid ... ...

    Abstract Using mel-spectrograms over conventional MFCCs features, we assess the abilities of convolutional neural networks to accurately recognize and classify emotions from speech data. We introduce FSER, a speech emotion recognition model trained on four valid speech databases, achieving a high-classification accuracy of 95,05\%, over 8 different emotion classes: anger, anxiety, calm, disgust, happiness, neutral, sadness, surprise. On each benchmark dataset, FSER outperforms the best models introduced so far, achieving a state-of-the-art performance. We show that FSER stays reliable, independently of the language, sex identity, and any other external factor. Additionally, we describe how FSER could potentially be used to improve mental and emotional health care and how our analysis and findings serve as guidelines and benchmarks for further works in the same direction.

    Comment: ABAW Workshop, ICCV 2021
    Schlagwörter Electrical Engineering and Systems Science - Audio and Speech Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Human-Computer Interaction
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2021-09-15
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  5. Buch ; Online: OkwuGb\'e

    Dossou, Bonaventure F. P. / Emezue, Chris C.

    End-to-End Speech Recognition for Fon and Igbo

    2021  

    Abstract: Language is inherent and compulsory for human communication. Whether expressed in a written or spoken way, it ensures understanding between people of the same and different regions. With the growing awareness and effort to include more low-resourced ... ...

    Abstract Language is inherent and compulsory for human communication. Whether expressed in a written or spoken way, it ensures understanding between people of the same and different regions. With the growing awareness and effort to include more low-resourced languages in NLP research, African languages have recently been a major subject of research in machine translation, and other text-based areas of NLP. However, there is still very little comparable research in speech recognition for African languages. Interestingly, some of the unique properties of African languages affecting NLP, like their diacritical and tonal complexities, have a major root in their speech, suggesting that careful speech interpretation could provide more intuition on how to deal with the linguistic complexities of African languages for text-based NLP. OkwuGb\'e is a step towards building speech recognition systems for African low-resourced languages. Using Fon and Igbo as our case study, we conduct a comprehensive linguistic analysis of each language and describe the creation of end-to-end, deep neural network-based speech recognition models for both languages. We present a state-of-art ASR model for Fon, as well as benchmark ASR model results for Igbo. Our linguistic analyses (for Fon and Igbo) provide valuable insights and guidance into the creation of speech recognition models for other African low-resourced languages, as well as guide future NLP research for Fon and Igbo. The Fon and Igbo models source code have been made publicly available.
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2021-03-13
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  6. Buch ; Online: Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation

    Dossou, Bonaventure F. P. / Emezue, Chris C.

    The Case of Fon Language

    2021  

    Abstract: Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge. Besides the issue of finding available resources for them, a lot of work is put into ... ...

    Abstract Building effective neural machine translation (NMT) models for very low-resourced and morphologically rich African indigenous languages is an open challenge. Besides the issue of finding available resources for them, a lot of work is put into preprocessing and tokenization. Recent studies have shown that standard tokenization methods do not always adequately deal with the grammatical, diacritical, and tonal properties of some African languages. That, coupled with the extremely low availability of training samples, hinders the production of reliable NMT models. In this paper, using Fon language as a case study, we revisit standard tokenization methods and introduce Word-Expressions-Based (WEB) tokenization, a human-involved super-words tokenization strategy to create a better representative vocabulary for training. Furthermore, we compare our tokenization strategy to others on the Fon-French and French-Fon translation tasks.
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2021-03-14
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  7. Buch ; Online: Lanfrica

    Emezue, Chris C. / Dossou, Bonaventure F. P.

    A Participatory Approach to Documenting Machine Translation Research on African Languages

    2020  

    Abstract: Over the years, there have been campaigns to include the African languages in the growing research on machine translation (MT) in particular, and natural language processing (NLP) in general. Africa has the highest language diversity, with 1500-2000 ... ...

    Abstract Over the years, there have been campaigns to include the African languages in the growing research on machine translation (MT) in particular, and natural language processing (NLP) in general. Africa has the highest language diversity, with 1500-2000 documented languages and many more undocumented or extinct languages(Lewis, 2009; Bendor-Samuel, 2017). This makes it hard to keep track of the MT research, models and dataset that have been developed for some of them. As the internet and social media make up the daily lives of more than half of the world(Lin, 2020), as well as over 40% of Africans(Campbell, 2019), online platforms can be useful in creating accessibility to researches, benchmarks and datasets in these African languages, thereby improving reproducibility and sharing of existing research and their results. In this paper, we introduce Lanfrica, a novel, on-going framework that employs a participatory approach to documenting researches, projects, benchmarks and dataset on African languages.
    Schlagwörter Computer Science - Computers and Society ; Computer Science - Computation and Language
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2020-08-03
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  8. Buch ; Online: FFR v1.1

    Dossou, Bonaventure F. P. / Emezue, Chris C.

    Fon-French Neural Machine Translation

    2020  

    Abstract: All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low- ... ...

    Abstract All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low-resourceness, diacritical, and tonal complexities of African languages are major issues being faced. The FFR project is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we introduce FFR Dataset, a corpus of Fon-to-French translations, describe the diacritical encoding process, and introduce our FFR v1.1 model, trained on the dataset. The dataset and model are made publicly available at https://github.com/ bonaventuredossou/ffr-v1, to promote collaboration and reproducibility.

    Comment: Accepted for publication at the Widening Natural Language Processing (WiNLP) Workshop, The 58th Annual Meeting of the Association for Computational Linguistics, 2020
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2020-06-14
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  9. Buch ; Online: FFR V1.0

    Dossou, Bonaventure F. P. / Emezue, Chris C.

    Fon-French Neural Machine Translation

    2020  

    Abstract: Africa has the highest linguistic diversity in the world. On account of the importance of language to communication, and the importance of reliable, powerful and accurate machine translation models in modern inter-cultural communication, there have been ( ...

    Abstract Africa has the highest linguistic diversity in the world. On account of the importance of language to communication, and the importance of reliable, powerful and accurate machine translation models in modern inter-cultural communication, there have been (and still are) efforts to create state-of-the-art translation models for the many African languages. However, the low-resources, diacritical and tonal complexities of African languages are major issues facing African NLP today. The FFR is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we describe our pilot project: the creation of a large growing corpora for Fon-to-French translations and our FFR v1.0 model, trained on this dataset. The dataset and model are made publicly available.

    Comment: Accepted for the AfricaNLP Workshop, ICLR 2020
    Schlagwörter Computer Science - Computation and Language
    Thema/Rubrik (Code) 410
    Erscheinungsdatum 2020-03-26
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  10. Buch ; Online: AfriNames

    Olatunji, Tobi / Afonja, Tejumade / Dossou, Bonaventure F. P. / Tonja, Atnafu Lambebo / Emezue, Chris Chinenye / Rufai, Amina Mardiyyah / Singh, Sahib

    Most ASR models "butcher" African Names

    2023  

    Abstract: Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a ... ...

    Abstract Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a laboratory result for a patient. However, where named entities such as ``Ukachukwu`` (Igbo), ``Lakicia`` (Swahili), or ``Ingabire`` (Rwandan) are spoken, automatic speech recognition (ASR) models' performance degrades significantly, propagating errors to downstream systems. We model this problem as a distribution shift and demonstrate that such model bias can be mitigated through multilingual pre-training, intelligent data augmentation strategies to increase the representation of African-named entities, and fine-tuning multilingual ASR models on multiple African accents. The resulting fine-tuned models show an 81.5\% relative WER improvement compared with the baseline on samples with African-named entities.

    Comment: Accepted at Interspeech 2023 (Main Conference)
    Schlagwörter Computer Science - Computation and Language ; Computer Science - Computers and Society
    Erscheinungsdatum 2023-05-31
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