Tunde Oluwaseyi Ajayi


2024

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Using Information Retrieval Techniques to Automatically Repurpose Existing Dialogue Datasets for Safe Chatbot Development
Tunde Oluwaseyi Ajayi | Gaurav Negi | Mihael Arcan | Paul Buitelaar
Proceedings of Safety4ConvAI: The Third Workshop on Safety for Conversational AI @ LREC-COLING 2024

There has been notable progress in the development of open-domain dialogue systems (chatbots) especially with the rapid advancement of the capabilities of Large Language Models. Chatbots excel at holding conversations in a manner that keeps a user interested and engaged. However, their responses can be unsafe, as they can respond in an offensive manner or offer harmful professional advice. As a way to mitigate this issue, recent work crowdsource datasets with exemplary responses or annotate dialogue safety datasets, which are relatively scarce compared to casual dialogues. Despite the quality of data obtained from crowdsourcing, it can be expensive and time consuming. This work proposes an effective pipeline, using information retrieval, to automatically repurpose existing dialogue datasets for safe chatbot development, as a way to address the aforementioned challenges. We select an existing dialogue dataset, revise its unsafe responses, as a way to obtain a dataset with safer responses to unsafe user inputs. We then fine-tune dialogue models on the original and revised datasets and generate responses to evaluate the safeness of the models.

2023

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Findings from the Bambara - French Machine Translation Competition (BFMT 2023)
Ninoh Agostinho Da Silva | Tunde Oluwaseyi Ajayi | Alexander Antonov | Panga Azazia Kamate | Moussa Coulibaly | Mason Del Rio | Yacouba Diarra | Sebastian Diarra | Chris Emezue | Joel Hamilcaro | Christopher M. Homan | Alexander Most | Joseph Mwatukange | Peter Ohue | Michael Pham | Abdoulaye Sako | Sokhar Samb | Yaya Sy | Tharindu Cyril Weerasooriya | Yacine Zahidi | Sarah Luger
Proceedings of the Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)

Orange Silicon Valley hosted a low-resource machine translation (MT) competition with monetary prizes. The goals of the competition were to raise awareness of the challenges in the low-resource MT domain, improve MT algorithms and data strategies, and support MT expertise development in the regions where people speak Bambara and other low-resource languages. The participants built Bambara to French and French to Bambara machine translation systems using data provided by the organizers and additional data resources shared amongst the competitors. This paper details each team’s different approaches and motivation for ongoing work in Bambara and the broader low-resource machine translation domain.