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 lowresource languages, including Indigenous languages.However, the latter are deeply related to the ethnic and cultural groups that speak (or used to speak) them.The data collection, modeling and deploying machine translation systems thus result in new ethical questions that must be addressed.Motivated by this, we first survey the existing literature on ethical considerations for the documentation, translation, and general natural language processing for Indigenous languages.Afterward, we conduct and analyze an interview study to shed light on the positions of community leaders, teachers, and language activists regarding ethical concerns for the automatic translation of their languages.Our results show that the inclusion, at different degrees, of native speakers and community members is vital to performing better and more ethical research on Indigenous languages.

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 translation (MT), morphological segmentation has proven successful in alleviating data sparsity in monolingual contexts; however, it has not been investigated for CS settings.In this paper, we study the effectiveness of different segmentation approaches on MT performance, covering morphology-based and frequency-based segmentation techniques.We experiment on MT from code-switched Arabic-English to English.We provide detailed analysis, examining a variety of conditions, such as data size and sentences with different degrees of CS.Empirical results show that morphology-aware segmenters perform the best in segmentation tasks but under-perform in MT.Nevertheless, we find that the choice of the segmentation setup to use for MT is highly dependent on the data size.For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best.For more resourced settings, such a combination does not bring significant improvements over the use of frequency-based segmentation.

Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Vladimir Meza Ruiz, Gustavo Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Thang Vu, Katharina Kann. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 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 the highresource languages German, English, and Indonesian to experiments on new datasets for the truly low-resource languages Popoluca and Tepehua.We explore two new models for the task, borrowing from the closely related area of morphological generation: an LSTM pointer-generator and a sequence-to-sequence model with hard monotonic attention trained with imitation learning.We find that, in the low-resource setting, the novel approaches outperform existing ones on all languages by up to 11.4% accuracy.However, while accuracy in emulated low-resource scenarios is over 50% for all languages, for the truly lowresource languages Popoluca and Tepehua, our best model only obtains 37.4% and 28.4% accuracy, respectively.Thus, we conclude that canonical segmentation is still a challenging task for low-resource languages.

Indigenous languages of the American continent are highly diverse. However, they have received little attention from the technological perspective. In this paper, we review the research, the digital resources and the available NLP systems that focus on these languages. We present the main challenges and research questions that arise when distant languages and low-resource scenarios are faced. We would like to encourage NLP research in linguistically rich and diverse areas like the Americas.

Katharina Kann, Jesus Manuel Mager Hois, Ivan Vladimir Meza-Ruiz, Hinrich Schütze. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.