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.

This article describes a new tool for extracting question-answer pairs from text articles, and reports three experiments which investigate how suitable this technique is for supplying knowledge to conversational characters. Experiment 1 demonstrates the feasibility of our method by creating characters for 14 distinct topics and evaluating them using hand-authored questions. Experiment 2 evaluates three of these characters using questions collected from naive participants, showing that the generated characters provide full or partial answers to about half of the questions asked. Experiment 3 adds automatically extracted knowledge to an existing, hand-authored character, demonstrating that augmented characters can answer questions about new topics but with some degradation of the ability to answer questions about topics that the original character was trained to answer. Overall, the results show that question generation is a promising method for creating or augmenting a question answering conversational character using an existing text.

We present techniques for the incremental interpretation and prediction of utterance meaning in dialogue systems. These techniques open possibilities for systems to initiate responsive overlap behaviors during user speech, such as interrupting, acknowledging, or completing a user’s utterance while it is still in progress. In an implemented system, we show that relatively high accuracy can be achieved in understanding of spontaneous utterances before utterances are completed. Further, we present a method for determining when a system has reached a point of maximal understanding of an ongoing user utterance, and show that this determination can be made with high precision. Finally, we discuss a prototype implementation that shows how systems can use these abilities to strategically initiate system completions of user utterances. More broadly, this framework facilitates the implementation of a range of overlap behaviors that are common in human dialogue, but have been largely absent in dialogue systems.