Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

Statistical Dialog Management for Health Interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

Two -way direction of influence in mother-infant interaction as denoted by concurrent reciprocal learning processes

This series of 5 single-subject studies used the operant conditioning paradigm to investigate, within the two-way influence process, how (a) contingent infant attention can reinforce maternal verbal behaviors during a period of mother-infant interaction and under subsequent experimental manipulation. Differential reinforcement was used to determine if it is possible that an infant attending to the mother (denoted by head-turns towards the image of the mother plus eye contact) increases (reinforces) the mother's verbal response (to a cue from the infant) upon which the infant behavior is contingent. There was also (b) an evaluation during the contrived parent-infant interaction for concurrent operant learning of infant vocal behavior via contingent verbal responding (reinforcement) implemented by the mother. Further, it was noted (c) whether or not the mother reported being aware that her responses were influenced by the infant's behavior. Findings showed: the operant conditioning of the maternal verbal behaviors were reinforced by contingent infant attention; and the operant conditioning of infant vocalizations was reinforced by contingent maternal verbal behaviors. No parent reported (1) being aware of the increase in their verbal response reinforced during operant conditioning of parental behavior nor a decrease in those responses during the DRA reversal phase, or (2) noticing a contingency between infant's and mother's response. By binomial 1-tail tests, the verbal-behavior patterns of the 5 mothers were conditioned by infant reinforcement (p < 0.02) and, concurrently, the vocal-response patterns of the 5 infants were conditioned by maternal reinforcement (p < 0.02). A program of systematic empirical research on the determinants of concurrent conditioning within mother-child interaction may provide a way to evaluate the differential effectiveness of interventions aimed at improving parent-child interactions. The work conducted in the present study is one step in this direction. ^