This paper investigates whether a multilingual spoken dialogue system can be used to help collect and preserve endangered language data. In this work, we extend DAPEL (Dialogue APp for Endangered Languages), which is designed to help preserve any language. Our focus, for testing purposes, is on the American Indigenous language Choctaw. The system uses English as a common language, and we test whether incorporating code-switching—the act of alternating between languages—enhances the user experience and/or increases the amount of recorded language data. Our results indicate that users have a positive response to interacting in both languages with the system, that the system plays a meaningful role in language documentation, and, notably, that participants who speak Choctaw as their first language are more receptive to a code-switching system than to a monolingual English-based system.

We investigate the learning outcomes and user response to a chatbot for practicing conversational Choctaw, an endangered American Indigenous language. Conversational fluency is a goal for many language learners, however, for learners of endangered languages in North America, access to fluent speakers may be limited. Chatbots are potentially ideal dialogue partners as this kind of dialogue system fulfills a non-authoritative role by focusing on carrying on a conversation as an equal conversational partner. The goal of the chatbot investigated in this work is to serve as a conversational partner in the absence of a fluent Choctaw-speaking human interlocutor. We investigate the impact of code-switching in the interaction, comparing a bilingual chatbot against a monolingual Choctaw version. We evaluate the systems for user engagement and enjoyment, as well as gains in conversational fluency from interacting with the system.

This work introduces additions to the corpus ChoCo, a multimodal corpus for the American indigenous language Choctaw. Using texts from the corpus, we develop new computational resources by using two off-the-shelf tools: word2vec and Linguistica. Our work illustrates how these tools can be successfully implemented with a small corpus.

We present the implementation of an autonomous chatbot, SHIHbot, deployed on Facebook, which answers a wide variety of sexual health questions on HIV/AIDS. The chatbot’s response database is com-piled from professional medical and public health resources in order to provide reliable information to users. The system’s backend is NPCEditor, a response selection platform trained on linked questions and answers; to our knowledge this is the first retrieval-based chatbot deployed on a large public social network.