Referential precedents in spoken language comprehension: a review and meta-analysis

Listeners’ interpretations of referring expressions are influenced by referential precedents—temporary conventions established in a discourse that associate linguistic expressions with referents. A number of psycholinguistic studies have investigated how much precedent effects depend on beliefs about the speaker’s perspective versus more egocentric, domain-general processes. We review and provide a meta-analysis of visual-world eyetracking studies of precedent use, focusing on three principal effects: (1) a same speaker advantage for maintained precedents; (2) a different speaker advantage for broken precedents; and (3) an overall main effect of precedents. Despite inconsistent claims in the literature, our combined analysis reveals surprisingly consistent evidence supporting the existence of all three effects, but with different temporal profiles. These findings carry important implications for existing theoretical explanations of precedent use, and challenge explanations based solely on the use of information about speakers’ perspectives

A corpus-based analysis of route instructions in human-robot interaction

This paper investigates how users employ spatial descriptions to navigate a speech-enabled robot. We created a simulated environment in which users gave route instructions in a dialogic real-time interaction with a robot, which was operated by naïve participants. The ability of robot monitoring was also manipulated in two experimental conditions. The results provide evidence that the content of the instructions and strategies of the users vary depending on the conditions and demands of the interaction. As expected, the route instructions frequently were underspecified and arbitrary. The findings of this study elucidate the complexity in interpreting spatial language in HRI. However, they also point to the need for endowing mobile robots with richer dialogue resources to compensate for the uncertainties arising from language as well as the environment

Do (and say) as I say: Linguistic adaptation in human-computer dialogs

© Theodora Koulouri, Stanislao Lauria, and Robert D. Macredie. This article has been made available through the Brunel Open Access Publishing Fund.There is strong research evidence showing that people naturally align to each other’s vocabulary, sentence structure, and acoustic features in dialog, yet little is known about how the alignment mechanism operates in the interaction between users and computer systems let alone how it may be exploited to improve the efficiency of the interaction. This article provides an account of lexical alignment in human–computer dialogs, based on empirical data collected in a simulated human–computer interaction scenario. The results indicate that alignment is present, resulting in the gradual reduction and stabilization of the vocabulary-in-use, and that it is also reciprocal. Further, the results suggest that when system and user errors occur, the development of alignment is temporarily disrupted and users tend to introduce novel words to the dialog. The results also indicate that alignment in human–computer interaction may have a strong strategic component and is used as a resource to compensate for less optimal (visually impoverished) interaction conditions. Moreover, lower alignment is associated with less successful interaction, as measured by user perceptions. The article distills the results of the study into design recommendations for human–computer dialog systems and uses them to outline a model of dialog management that supports and exploits alignment through mechanisms for in-use adaptation of the system’s grammar and lexicon

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Evaluating Competing Agent Strategies for a Voice Email Agent

This paper reports experimental results comparing a mixed-initiative to a system-initiative dialog strategy in the context of a personal voice email agent. To independently test the effects of dialog strategy and user expertise, users interact with either the system-initiative or the mixed-initiative agent to perform three successive tasks which are identical for both agents. We report performance comparisons across agent strategies as well as over tasks. This evaluation utilizes and tests the PARADISE evaluation framework, and discusses the performance function derivable from the experimental data.Comment: 6 pages latex, uses icassp91.sty, psfi

PRESENCE: A human-inspired architecture for speech-based human-machine interaction

Recent years have seen steady improvements in the quality and performance of speech-based human-machine interaction driven by a significant convergence in the methods and techniques employed. However, the quantity of training data required to improve state-of-the-art systems seems to be growing exponentially and performance appears to be asymptotic to a level that may be inadequate for many real-world applications. This suggests that there may be a fundamental flaw in the underlying architecture of contemporary systems, as well as a failure to capitalize on the combinatorial properties of human spoken language. This paper addresses these issues and presents a novel architecture for speech-based human-machine interaction inspired by recent findings in the neurobiology of living systems. Called PRESENCE-"PREdictive SENsorimotor Control and Emulation" - this new architecture blurs the distinction between the core components of a traditional spoken language dialogue system and instead focuses on a recursive hierarchical feedback control structure. Cooperative and communicative behavior emerges as a by-product of an architecture that is founded on a model of interaction in which the system has in mind the needs and intentions of a user and a user has in mind the needs and intentions of the system