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Learning effective and engaging strategies for advice-giving human-machine dialogue
Spitters M., De Boni M., Zavrel J., Bonnema R. Natural Language Engineering15 (3):355-378,2009.Type:Article
Date Reviewed: Dec 28 2009

Human-machine dialogue is gaining importance as a means to improve natural language interfaces, user interpretations of machine languages, and the usability of computer systems. Due to advancements in technology, human-computer interfaces are dynamic and the human needs are of varying degrees. Therefore, this area of study mostly focuses on conceptualization, development, and the implementation of innovative models to determine the optimal fitness of the interfaces.

In this paper, Spitters et al. focus on dialogue strategy, which is quite important in a dialogue system, in order to fruitfully engage users with machines. The authors lucidly present a model for possible dialogues, system architecture, and process flows, and also keep in mind the social and relational interactions of users with machines. The paper also considers various dimensions of user problems, including user ambiguity, user barrier, and negation, in order to enrich the dialogue process. For clarity, these dimensions are presented through algorithms. An empirical analysis explains the appropriateness of the model, which increases the clarity of the model’s applicability.

However, the paper lacks a careful comparison of this model with other standard approaches. An algorithmic presentation of each of the elements of the model would have shown its relevance and applicability more clearly.

Overall, the paper provides a good understanding of the human-machine dialogue process. It will be helpful to researchers who are interested in using the model and/or conducting further experiments in this field.

Reviewer:  Harekrishna Misra Review #: CR137597 (1006-0616)
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