AmericasNLP 2025 Shared Task 1: Machine Translation Systems for Indigenous Languages

What?

The AmericasNLP 2025 Shared Task on machine translation systems for Indigenous languages is a competition aimed at encouraging the development of machine translation (MT) systems for Indigenous languages of the Americas. Participants will build systems that translate between Spanish and an Indigenous language.

Why?

Many of the Indigenous languages of the Americas are so-called low-resource languages: parallel data with other languages as needed to train MT systems is limited. This means that many approaches designed for translating between high-resource languages, such as English and Chinese, are not directly applicable or perform poorly. Additionally, many Indigenous languages exhibit linguistic properties uncommon among languages frequently studied in natural language processing (NLP). For instance, many are polysynthetic. This constitutes an additional difficulty. The goal of the AmericasNLP 2025 shared task on machine translation systems for Indigenous languages is to motivate researchers to take on the challenge of developing MT systems for Indigenous languages.

How?

For 2025, we are including new languages, and we are also expanding the shared task to include both translation into an Indigenous language (from Spanish) as well as translation from an Indigenous language into Spanish. These different directions will be represented as two tracks within the shared task: More information on the GitHub page.

Which languages?

The following language pairs are featured in the shared task:

Important Dates

All deadlines are 11:59 pm UTC -12h (AoE).

Organizers

Abteen Ebrahimi, Arturo Oncevay, Pavel Denisov, Robert Pugh, Ona de Gibert Bonet, Raúl Vázquez, Manuel Mager, Luis Chiruzzo, Rolando Coto-Solano, Katharina von der Wense, Shruti Rijhwani

Contact: americas.nlp.workshop@gmail.com
Design: Rebeca Guerrero and Manuel Mager