In this paper, we describe a general-purpose dialogue management framework used to design SOPHIE (Standardized Online Patient for Healthcare Interaction Education). SOPHIE simulates a virtual standardized cancer patient that allows physicians to practice skills such as empathy and patient empowerment in end-of-life communication. To provide the user with an opportunity to practice these skills, SOPHIE must produce a natural, emotionally appropriate conversation, yet handle topic shifts and open-ended questions from the user. To accomplish this, our approach to dialogue management loosely followsschemas– explicit representations of thetypicalflows of dialogue in end-of-life communication – while also using flexible pattern-driven methods for interpretation and generation. We conduct a crowdsourced evaluation of conversations between medical students and SOPHIE. Our agent is judged to produce responses that are natural, emotionally appropriate, and consistent with her role as a cancer patient. Furthermore, it significantly outperforms an end-to-end neural model fine-tuned on a human standardized patient corpus, attesting to the advantages of a schema-guided approach in this domain. However, the system is currently limited in its ability to generate responses that are judged to demonstrate deep understanding of the user, suggesting that future work should place focus on integrating this framework with robust natural language understanding and commonsense reasoning methods.

Effective communication between a clinician and their patient is critical for delivering healthcare maximizing outcomes. Unfortunately, traditional communication training approaches that use human standardized patients and expert coaches are difficult to scale. Here, we present the development and validation of a scalable, easily accessible, digital tool known as the Standardized Online Patient for Health Interaction Education (SOPHIE) for practicing and receiving feedback on doctor-patient communication skills. SOPHIE was validated by conducting an experiment with 30 medical professionals. We found that participants who underwent SOPHIE condition performed significantly better than the control in overall communication, aggregate scores, empowering the patient, and showing empathy (p < 0.05 in all cases). The results presented in this paper provide early evidence of scalable and accessible virtual humans, chatbot technologies, and automated feedback generation being transformative for medical communication training by supplementing existing resources.

Many practical applications of dialogue technology require the generation of responses according to a particular developer-specified persona.While a variety of personas can be elicited from recent large language models, the opaqueness and unpredictability of these models make it desirable to be able to specify personas in an explicit form.In previous work, personas have typically been represented as sets of one-off pieces of self-knowledge that are retrieved by the dialogue system for use in generation.However, in realistic human conversations, personas are often revealed through story-like narratives that involve rich habitual knowledge -knowledge about kinds of events that an agent often participates in (e.g., work activities, hobbies, sporting activities, favorite entertainments, etc.), including typical goals, subevents, preconditions, and postconditions of those events.We capture such habitual knowledge using an explicit schema representation, and propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate personabased responses.Furthermore, we demonstrate a method for bootstrapping the creation of such schemas by first generating generic passages from a set of simple facts, and then inducing schemas from the generated passages.

In this article, we describe the iterative participatory design of SOPHIE, an online virtual patient for feedback-based practice of sensitive patient-physician conversations, and discuss an initial qualitative evaluation of the system by professional end users. The design of SOPHIE was motivated from a computational linguistic analysis of the transcripts of 383 patient-physician conversations from an essential office visit of late stage cancer patients with their oncologists. We developed methods for the automatic detection of two behavioral paradigms, lecturing and positive language usage patterns (sentiment trajectory of conversation), that are shown to be significantly associated with patient prognosis understanding. These automated metrics associated with effective communication were incorporated into SOPHIE, and a pilot user study identified that SOPHIE was favorably reviewed by a user group of practicing physicians.

Robots operating in unexplored environments with human teammates will need to learn unknown concepts on the fly.To this end, we demonstrate a novel system that combines a computational model of question generation with a cognitive robotic architecture.The model supports dynamic production of backand-forth dialogue for concept learning given observations of an environment, while the architecture supports symbolic reasoning, action representation, one-shot learning and other capabilities for situated interaction.The system is able to learn about new concepts including objects, locations, and actions, using an underlying approach that is generalizable and scalable.We evaluate the system by comparing learning efficiency to a human baseline in a collaborative reference resolution task and show that the system is effective and efficient in learning new concepts, and that it can informatively generate explanations about its behavior. * Work performed during a summer position at the Army Research Laboratory.1 The term 'concept' in this paper refers to any entity in the task domain, including objects, locations, and actions.. . .

We present a conversational agent designed to provide realistic conversational practice to older adults at risk of isolation or social anxiety, and show the results of a content analysis on a corpus of data collected from experiments with elderly patients interacting with our system. The conversational agent, represented by a virtual avatar, is designed to hold multiple sessions of casual conversation with older adults. Throughout each interaction, the system analyzes the prosodic and nonverbal behavior of users and provides feedback to the user in the form of periodic comments and suggestions on how to improve. Our avatar is unique in its ability to hold natural dialogues on a wide range of everyday topics—27 topics in three groups, developed in collaboration with a team of gerontologists. The three groups vary in “degrees of intimacy,” and as such in degrees of cognitive difficulty for the user. After collecting data from nine participants who interacted with the avatar for seven to nine sessions over a period of 3 to 4 weeks, we present results concerning dialogue behavior and inferred sentiment of the users. Analysis of the dialogues reveals correlations such as greater elaborateness for more difficult topics, increasing elaborateness with successive sessions, stronger sentiments in topics concerned with life goals rather than routine activities, and stronger self-disclosure for more intimate topics. In addition to their intrinsic interest, these results also reflect positively on the sophistication and practical applicability of our dialogue system.

We present the design of an online social skills development interface for teenagers with autism spectrum disorder (ASD). The interface is intended to enable private conversation practice anywhere, anytime using a web-browser. Users converse informally with a virtual agent, receiving feedback on nonverbal cues in realtime, and summary feedback. The prototype was developed in consultation with an expert UX designer, two psychologists, and a pediatrician. Using the data from 47 individuals, feedback and dialogue generation were automated using a hidden Markov model and a schema-driven dialogue manager capable of handling multi-topic conversations. We conducted a study with nine high-functioning ASD teenagers. Through a thematic analysis of post-experiment interviews, identified several key design considerations, notably:

A physical blocks world, despite its relative simplicity, requires (in fully interactive form) a rich set of functional capabilities, ranging from vision to natural language understanding.In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling.The contributions of this work include a semantic parser that maps spatial questions into logical forms consistent with a general approach to meaning representation, a dialogue manager based on a schema representation, and a constraint solver for spatial questions that provides answers in agreement with human perception.These and other components are integrated into a multi-modal human-computer interaction pipeline.

Social media has become a ubiquitous part of the lives of many people, and provides a channel for ordinary people to voice and to hear political opinions. However, many believe that the rise of social media has lead to an increasing polarization of political views, with political bias becoming intertwined with seemingly non-political interests and behavior. In this paper, we aim to use natural language processing techniques to analyze the political bias of online social groups and the degree to which this bias correlates with non-political topics. Whereas this phenomenon has been studied extensively on networks such as Twitter, the popular social media domain Reddit has been relatively unexplored despite the structure of the platform allowing users to easily create their own interest-based subcommunities, thus providing an important and unique data source relevant to the topic at hand. This paper analyzes comment data from approximately 3,300 message boards on Reddit, with the goal of providing novel empirical knowledge about how group topic informs political bias in Reddit sub-communities. Topics of Reddit subcommunities are determined using a Latent Dirichlet Allocation model, and political bias of subcommunities is measured through direct comparison with external corpora of politically biased vocabulary; results are discussed within. Furthermore, to test the politicization of topics, we train a classifier on topic features to obtain a high accuracy of 85.2% (compared to a random-guessing baseline of 64.8%), suggesting fairly strong correlations between group topic and politically biased language in communities.