AmericasNLP
Organizers: Manuel Mager, Arturo Oncevay, Enora Rice, Abteen Ebrahimi, Shruti Rijhwani, Alexis Palmer, Katharina Kann
Workshop Papers
Authors: Begoa Pendas, Andres Carvallo, Carlos Aspillaga
Active learning is an algorithmic approach that strategically selects a subset of examples for labeling, with the goal of reducing workload and required resources. Previous research has applied active learning to Neural Machine Translation (NMT) for high-resource or well-represented languages, achieving significant reductions in manual labor. In this study, we explore the application of active learning for NMT in the context of Mapudungun, a low-resource language spoken by the Mapuche community in South America. Mapudungun was chosen due to the limited number of fluent speakers and the pressing need to provide access to content predominantly available in widely represented languages. We assess both model-dependent and model-agnostic active learning strategies for NMT between Spanish and Mapudungun in both directions, demonstrating that we can achieve over 40\% reduction in manual translation workload in both cases.
Go to PaperAuthors: Paulo Cavalin, Pedro Domingues, Julio Nogima, Claudio Pinhanez
We investigate native language identification (LangID) for Brazilian Indigenous Languages (BILs), using the Bible as training data. Our research extends from previous work, by presenting two analyses on the generalization of Bible-based LangID in non-biblical data. First, with newly collected non-biblical datasets, we show that such a LangID can still provide quite reasonable accuracy in languages for which there are more established writing standards, such as Guarani Mbya and Kaigang, but there can be a quite drastic drop in accuracy depending on the language. Then, we applied the LangID on a large set of texts, about 13M sentences from the Portuguese Wikipedia, towards understanding the difficulty factors may come out of such task in practice. The main outcome is that the lack of handling other American indigenous languages can affect considerably the precision for BILs, suggesting the need of a joint effort with related languages from the Americas.
Go to PaperAuthors: Atnafu Lambebo Tonja, Christian Maldonado-sifuentes, David Alejandro Mendoza Castillo, Olga Kolesnikova, No Castro-snchez, Grigori Sidorov, Alexander Gelbukh
In this paper, we present a parallel Spanish- Mazatec and Spanish-Mixtec corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two indigenous Mexican languages. We evaluated the usability of the collected corpus using three different approaches: transformer, transfer learning, and fine-tuning pre-trained multilingual MT models. Fine-tuning the Facebook m2m100-48 model outperformed the other approaches, with BLEU scores of 12.09 and 22.25 for Mazatec-Spanish and Spanish-Mazatec translations, respectively, and 16.75 and 22.15 for Mixtec-Spanish and Spanish-Mixtec translations, respectively. The results indicate that translation performance is influenced by the dataset size (9,799 sentences in Mazatec and 13,235 sentences in Mixtec) and is more effective when indigenous languages are used as target languages. The findings emphasize the importance of creating parallel corpora for indigenous languages and fine-tuning models for low-resource translation tasks. Future research will investigate zero-shot and few-shot learning approaches to further improve translation performance in low-resource settings.
Go to PaperAuthors: Benjamin Hunt, Lane Schwartz, Sylvia Schreiner, Emily Chen
In this paper, we present a new online dictionary of Akuzipik, an Indigenous language of St. Lawrence Island (Alaska) and Chukotka (Russia).We discuss community desires for strengthening language use in the community and in educational settings, and present specific features of an online dictionary designed to serve these community goals.
Go to PaperAuthors: Nouman Ahmed, Natalia Flechas Manrique, Antonije Petrovi
We present the LCT-EHU submission to the AmericasNLP 2023 low-resource machine translation shared task. We focus on the Spanish-Quechua language pair and explore the usage of different approaches: (1) Obtain new parallel corpora from the literature and legal domains, (2) Compare a high-resource Spanish-English pre-trained MT model with a Spanish-Finnish pre-trained model (with Finnish being chosen as a target language due to its morphological similarity to Quechua), and (3) Explore additional techniques such as copied corpus and back-translation. Overall, we show that the Spanish-Finnish pre-trained model outperforms other setups, while low-quality synthetic data reduces the performance.
Go to PaperAuthors: Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Winata, Bryan Wilie, Fajri Koto, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio
We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments.NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken.
Go to PaperAuthors: Tianrui Gu, Kaie Chen, Siqi Ouyang, Lei Li
This paper presents PlayGround's submission to the AmericasNLP 2023 shared task on machine translation (MT) into indigenous languages. We finetuned NLLB-600M, a multilingual MT model pre-trained on Flores-200, on 10 low-resource language directions and examined the effectiveness of weight averaging and back translation. Our experiments showed that weight averaging, on average, led to a 0.0169 improvement in the ChrF++ score. Additionally, we found that back translation resulted in a 0.008 improvement in the ChrF++ score.
Go to PaperAuthors: Abteen Ebrahimi, Manuel Mager, Shruti Rijhwani, Enora Rice, Arturo Oncevay, Claudia Baltazar, María Cortés, Cynthia Montaño, John E. Ortega, Rolando Coto-solano, Hilaria Cruz, Alexis Palmer, Katharina Kann
In this work, we present the results of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages of the Americas. This edition of the shared task featured eleven language pairs, one of which – Chatino-Spanish – uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain. Seven teams participated in the shared task, with a total of 181 submissions. Additionally, we conduct a human evaluation of the best system outputs, and compare them to the best submissions from the prior shared task. We find that this analysis agrees with the quantitative measures used to rank submissions, which shows further improvements of 9.64 ChrF on average across all languages, when compared to the prior winning system.
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