Most harmful dialogue detection models are developed for high-resourced languages. Consequently, users who speak under-resourced languages cannot fully benefit from these models in terms of usage, development, detection and mitigation of harmful dialogue utterances. Our work aims at detecting harmful utterances in under-resourced African languages. We leverage transfer learning using pretrained models trained with multilingual embeddings to develop a cross-lingual model capable of detecting harmful content across various African languages. We first fine-tune a harmful dialogue detection model on a selected African dialogue dataset. Additionally, we fine-tune a model on a combined dataset in some African languages to develop a multilingual harmful dialogue detection model. We then evaluate the cross-lingual model’s ability to generalise to an unseen African language by performing harmful dialogue detection in an under-resourced language not present during pretraining or fine-tuning. We evaluate our models on the test datasets. We show that our best performing models achieve impressive results in terms of F1 score. Finally, we discuss the results and limitations of our work.

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.

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.