Manuel Mager

Tackling the Low-resource Challenge for Canonical Segmentation

2020

Canonical morphological segmentation consists of dividing words into their standardized morphemes. Here, we are interested in approaches for the task when training data is limited. We compare model performance in a simulated low-resource setting for …

BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages

2022 · Findings of the Association for Computational Linguistics: ACL 2022

Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation. We investigate a wide variety of supervised and unsupervised …

Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers

2023

In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-resource languages, including Indigenous languages. However, the latter are …