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 effectively adapting loanwords during the translation process.To tackle this, we propose a novel approach that augments source sentences with loanword constraints, explicitly linking source-language loanwords to their target-language equivalents.Then, we perform supervised fine-tuning on multilingual neural machine translation models and multiple Large Language Models of different sizes.Our results demonstrate that incorporating loanword constraints leads to significant improvements in translation quality as well as in handling loanword adaptation correctly in target languages, as measured by different machine translation metrics.This approach offers a promising direction for improving machine translation performance in low-resource settings characterized by frequent lexical borrowing.

Senyu Li, Jiayi Wang, Felermino D. M. A. Ali, Colin Cherry, Daniel Deutsch, Eleftheria Briakou, Rui Sousa-Silva, Henrique Lopes Cardoso, Pontus Stenetorp, David Ifeoluwa Adelani. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

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 of low-resource languages that lack standardized spelling conventions.This research proposes a supervised method to identify loanwords in Emakhuwa, borrowed from Portuguese. Our methodology encompasses a two-fold approach. Firstly, we employ traditional machine learning algorithms incorporating handcrafted features, including language-specific and similarity-based features. We build upon prior studies to extract similarity features and propose utilizing two external resources: a Sequence-to-Sequence model and a dictionary. This innovative approach allows us to identify loanwords solely by analyzing the target word without prior knowledge about its donor counterpart. Furthermore, we fine-tune the pre-trained CANINE model for the downstream task of loanword detection, which culminates in the impressive achievement of the F1-score of 93%. To the best of our knowledge, this study is the first of its kind focusing on Emakhuwa, and the preliminary results are promising as they pave the way to further advancements.

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 process and challenges of acquiring this data and present benchmark results for machine translation and news topic classification tasks.Our evaluation examines the impact of different data types-originally clean text, postcorrected OCR, and back-translated data-and the effects of fine-tuning from pre-trained models, including those focused on African languages.Our benchmarks demonstrate good performance in news topic classification and promising results in machine translation.We fine-tuned multilingual encoder-decoder models using real and synthetic data and evaluated them on our test set and the FLORES evaluation sets.The results highlight the importance of incorporating more data and potential for future improvements.All models, code, and datasets are available in the https://huggingface.

Abstract The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora.

Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.