65 citations · 84 across the 9 of their papers we have counts for
13 papers
Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction
Manuel Mager, Rajat Bhatnagar, Graham Neubig +2
Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training…
Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers
Manuel Mager, Elisabeth Mager, Katharina Kann +1
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-…
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text
Marwa Gaser, Manuel Mager, Injy Hamed +3
Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages. For the task of machine transl…
BPE vs. Morphological Segmentation: A Case Study on Machine Translation of Four Polysynthetic Languages
Manuel Mager, Arturo Oncevay, Elisabeth Mager +2
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.…
IMS' Systems for the IWSLT 2021 Low-Resource Speech Translation Task
Pavel Denisov, Manuel Mager, Ngoc Thang Vu
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augment…
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Abteen Ebrahimi, Manuel Mager, Arturo Oncevay +14
Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating perfo…