6 accepted papers
This research investigates how to improve machine translation systems for low-resource languages by integrating loanword constraints as external linguistic knowledge. Focusing on the Portuguese-Emakhuwa language pair, which exhibits significant lexical borrowing, we address the challenge of effectiv
Paraphrasing is rooted in semantics, which makes evaluating paraphrase generation systems hard. Current paraphrase generators are typically evaluated using borrowed metrics from adjacent text-to-text tasks, like machine translation or text summarization. These metrics tend to have ties to the surfac…
Evaluating machine translation (MT) quality for under-resourced African languages remains a significant challenge, as existing metrics often suffer from limited language coverage and poor performance in low-resource settings. While recent efforts, such as AfriCOMET, have addressed some of the issues
Multimodal models, namely vision-language models, present unique possibilities through the seamless integration of different information mediums for data generation. These models mostly act as a black-box, making them lack transparency and explicability. Reliable results require accountable and trus…
This paper introduces a comprehensive collection of NLP resources for Emakhuwa, Mozambique’s most widely spoken language. The resources include the first manually translated news bitext corpus between Portuguese and Emakhuwa, news topic classification datasets, and monolingual data. We detail the pr…
The accurate identification of loanwords within a given text holds significant potential as a valuable tool for addressing data augmentation and mitigating data sparsity issues. Such identification can improve the performance of various natural language processing tasks, particularly in the context…