Victor Chou Hung

Towards a Context-Based Dialog Management Layer for Expert Systems

Speech-based conversation agents describe those computer-based entities that interact with humans to help accomplish a certain task via spoken word input. This paper proposes a method of managing spoken dialog interactions in response to recognizing the human users goals when accessing an expert system. In particular, a set of goals can co-exist during a single conversation, and that each goal may be presented in an asynchronous manner. Such a stipulation exists to enhance the naturalness of the interaction. Inspired by the Context-Based Reasoning paradigm, the Lifelike dialog system described herein features a goal management system that ultimately controls the behavior of the expert system.

Towards a method for evaluating naturalness in conversational dialog systems

The evaluation of conversational dialog systems has remained a controversial topic, as it is challenging to quantitatively assess how well a conversation agent performs, or how much better one is compared to another. Furthermore, one of the hurdles which remains elusive in this quandary is the definition of naturalness, as demonstrated by how well a dialog system can maintain a natural conversation flow devoid of perceived awkwardness. As a step towards defining the dimensions of effectiveness and naturalness in a dialog system, this paper identifies existing evaluation practices which are then expanded to develop a more suitable assessment vehicle. This method is then applied to the LifeLike virtual avatar project.

Context‐Centric Speech‐Based Human–Computer Interaction

This paper describes research that addresses the problem of dialog management from a strong, context‐centric approach. We further present a quantitative method of measuring the importance of contextual cues when dealing with speech‐based human–computer interactions. It is generally accepted that using context in conjunction with a human input, such as spoken speech, enhances a machines understanding of the users intent as a means to pinpoint an adequate reaction. For this work, however, we present a context‐centric approach in which the use of context is the primary basis for understanding and not merely an auxiliary process. We employ an embodied conversation agent that facilitates the seamless engagement of a speech‐based information‐deployment entity by its human end user. This dialog manager emphasizes the use of context to drive its mixed‐initiative discourse model. A typical, modern automatic speech recognizer (ASR) was incorporated to handle the speech‐to‐text translations. As is the nature of these ASR systems, the recognition rate is consistently less than perfect, thus emphasizing the need for contextual assistance. The dialog system was encapsulated into a speech‐based embodied conversation agent platform for prototyping and testing purposes. Experiments were performed to evaluate the robustness of its performance, namely through measures of naturalness and usefulness, with respect to the emphasized use of context. The contribution of this work is to provide empirical evidence of the importance of conversational context in speech‐based human–computer interaction using a field‐tested context‐centric dialog manager.

Passing an Enhanced Turing Test – Interacting with Lifelike Computer Representations of Specific Individuals

Abstract This article describes research to build an embodied conversational agent (ECA) as an interface to a question-and-answer (Q/A) system about a National Science Foundation (NSF) program. We call this ECA the LifeLike Avatar, and it can interact with its users in spoken natural language to answer general as well as specific questions about specific topics. In an idealized case, the LifeLike Avatar could conceivably provide a user with a level of interaction such that he or she would not be certain as to whether he or she is talking to the actual person via video teleconference. This could be considered a (vastly) extended version of the seminal Turing test. Although passing such a test is still far off, our work moves the science in that direction. The Uncanny Valley notwithstanding, applications of such lifelike interfaces could include those where specific instructors/caregivers could be represented as stand-ins for the actual person in situations where personal representation is important. Possible areas that come to mind that might benefit from these lifelike ECAs include health-care support for elderly/disabled patients in extended home care, education/training, and knowledge preservation. Another more personal application would be to posthumously preserve elements of the persona of a loved one by family members. We apply this approach to a Q/A system for knowledge preservation and dissemination, where the specific individual who had this knowledge was to retire from the US National Science Foundation. The system is described in detail, and evaluations were performed to determine how well the system was perceived by users.

A Knowledge Preservation and Re-Use Tool Based on Context-Driven Reasoning

This article describes a knowledge preservation and re-use tool designed to capture the knowledge of a specific individual at the US National Science Foundation, for later retrieval by successors after his retirement. The system is designed in a Q&A format, where it is sufficiently intelligent to ask for clarifying questions. The primary objective was to create a system that would result in acceptance of the system by the users. The domain of interest to be preserved and re-used was programmatic knowledge about the NSF Industry/University Collaborative Research Centers (I/UCRC) Program, and more specifically, the knowledge of its long-time director, Dr. Alex Schwarzkopf. The system is called AskAlex and it uses a trio of techniques to accomplish its objectives. Contextual graphs (CxG) are used as the basic knowledge representation structure. CxG’s are assisted by a search engine and an ontology of terms to help find the proper contextual graph that can best answer the question being asked. Evaluations with users and potential users generally confirm our selection and provided some guidance for improvements in the system.

Context and NLP

Early Natural Language Processing (NLP) endeavors often employed contextual cues as supplemental assistive measures—secondary sources of data to help understand its users’ linguistic inputs. Context was used more as a tie-breaking tool rather than as a central component in conversational negotiation. Recent work in context-based reasoning has inspired a paradigm shift from these context-assisted techniques to context-centric NLP systems. This evolution of context’s role in NLP is necessary to support today’s sophisticated Human-Computer Interaction (HCI) applications, such as personal digital assistants, language tutors, and question answering systems. In these applications, there is a strong sense of utilitarian, purpose-driven conversation. Such an emphasis on goal-oriented behavior requires that the underlying NLP methods be capable of navigating through a conversation at the conceptual, or contextual level. This chapter explores the natural bond between NLP and context-based methods, as it manifests itself in the context-centric paradigm. Insights and examples are provided along the way to shed light on this evolved way of engineering natural language-based HCI.