Building an endangered language resource in the classroom: Universal dependencies for Kakataibo

In this paper, we launch a new Universal Dependencies treebank for an endangered language from Amazonia: Kakataibo, a Panoan language spoken in Peru. We first discuss the collaborative methodology implemented, which proved effective to create a treebank in the context of a Computational Linguistic course for undergraduates. Then, we describe the general details of the treebank and the language-specific considerations implemented for the proposed annotation. We finally conduct some experiments on part-of-speech tagging and syntactic dependency parsing. We focus on monolingual and transfer learning settings, where we study the impact of a Shipibo-Konibo treebank, another Panoan language resourc

Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models

Large multilingual models have inspired a new class of word alignment methods, which work well for the model's pretraining languages. However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data. In this work, we ask: How do modern aligners perform on unseen languages, and are they better than traditional methods? We contribute gold-standard alignments for Bribri--Spanish, Guarani--Spanish, Quechua--Spanish, and Shipibo-Konibo--Spanish. With these, we evaluate state-of-the-art aligners with and without model adaptation to the target language. Finally, we also evaluate the resulting alignments extrinsically through two downstream tasks: named entity recognition and part-of-speech tagging. We find that although transformer-based methods generally outperform traditional models, the two classes of approach remain competitive with each other.Comment: EACL 202

Overview de ReCoRES en IberLEF 2022: Comprensión de Lectura y Explicación de Razonamiento en Español

This paper presents the ReCoRES task, organized at IberLEF 2022, within the framework of the 38th edition of the International Conference of the Spanish Society for Natural Language Processing. The main goal of this shared-task is to promote the task of Reading Comprehension and Verbal Reasoning. This task is divided into two sub-tasks: (1) identifying the correct alternative in reading comprehension questions and (2) generating the reasoning used to select an alternative. In general, 3 teams participated in this event, mainly proposing transformer-based neural models in conjunction with additional strategies. The results of this event, insights and some challenges are presented, opening a range of possibilities for future work.Este artículo presenta la tarea ReCoRES, organizada en IberLEF 2022, en el marco de la 38 edición de la Conferencia Internacional de la Sociedad Española para el Procesamiento del Lenguaje Natural. El objetivo de esta tarea es promover la tarea de Comprensión de Lectura y Razonamiento Verbal. Esta tarea es dividida en dos sub-tareas: (1) la identificación de la alternativa correcta en preguntas de comprensión de lectura y (2) la generación del razonamiento usado para seleccionar una alternativa. En general, 3 equipos participaron de este evento proponiendo mayormente modelos neuronales basados en transformers con algunas estrategias adicionales. Los resultados de este evento así como aprendizajes y algunos desafíos son presentados, abriendo un abanico de posibilidades como trabajos futuros

Open machine translation for low resource South American languages (AmericasNLP 2021 shared task contribution)

This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs “Spanish-Bribri”, “Spanish-Asháninka”, and “Spanish-Rarámuri” in the category “Development set not used for training”. Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.This paper describes the team (“Tamalli”)’s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs “Spanish-Bribri”, “Spanish-Asháninka”, and “Spanish-Rarámuri” in the category “Development set not used for training”. Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.C