Welcome, workshop schedule, and competition overview.

Natural language processing (NLP) technology has seen tremendous improvements in recent years but most of these successes have been concentrated in languages with large amounts of data. In this talk, I will discuss challenges and potential solutions on the way to scaling NLP to more of the world's 7000 languages. In particular, I will highlight recent progress in NLP for African languages and present methods that are applicable to languages with limited data such as employing alternative sources of data and multi-modal information.

The teams who have participated in the Second AmericasNLP Competition: Speech-to-Text Translation for Indigenous Languages of the Americas present their approaches, 1 poster per team.

Natural Language Processing researchers and computational linguists frequently express disappointment and frustration over the lack of corpus in endangered languages that they can use to train and test their language models. This hindrance, caused in large part by a dwindling number of speakers and language keepers to create new data such as stories, prayers, political speeches, and everyday conversation. Coupled with this is the severe lack of capacity among speakers of endangered languages to prepare a corpus including transcribers, annotators, and translators. What can NLP researchers do to help create and facilitate the corpus in these languages? Collaborating with communities to increase capacity to develop corpora with members would be a first step. Furthermore, teaching basic programming courses in local high schools and colleges, working with legacy materials in language archives, and doing fieldwork to collect data alongside community members would greatly enhance the creation of endangered language corpora for NLP.