A Language-Based Model for Specifying and Staging Mixed-Initiative Dialogs

Specifying and implementing flexible human-computer dialogs, such as those used in kiosks, is complex because of the numerous and varied directions in which each user might steer a dialog. The objective of this research is to improve dialog specification and implementation. To do so we developed a model for specifying and staging mixed-initiative dialogs. The model involves a dialog authoring notation, based on concepts from programming languages, for specifying a variety of unsolicited reporting, mixed-initiative dialogs in a concise representation that serves as a design for dialog implementation. Guided by this foundation, we built a dialog staging engine which operationalizes dialogs specified in this notation. The model, notation, and engine help automate the engineering of mixed-initiative dialog systems. These results also provide a proof-of-concept for dialog specification and implementation from the perspective of theoretical programming languages. The ubiquity of dialogs in domains such as travel, education, and health care with the increased use of interactive voice-response systems and virtual environments provide a fertile landscape for further investigation of these results

Creating Conversational Characters Using Question Generation Tools

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

Creating conversational characters using question generation tools

Abstract. This article describes a new question generation tool for extracting question-answer databases from text articles, and reports three experiments which investigate how suitable this technique is for giving domain-specific 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

Creating conversational characters using question generation tools

Abstract This article describes a new tool for extracting question-answer pairs from text articles, and reports three experiments that 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