By Frédéric Landragin(auth.)
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Additional info for Man-Machine Dialogue
In MMD, for example, the Web service Ananova takes on the appearance of a gorgeous young lady . . with green hair [HAR 05, p. 341]. Finally, a key challenge for the abilities of an MMD system is its robustness, that is its ability to manage its own shortfalls, at a linguistic analysis level for example, its own deﬁciencies and errors, and its ability to always bounce back, to help the dialogue progress no matter what the cost, by using the task to be solved, or not. This implies the design of modules able to operate with incomplete entries and have strategies to manage problems.
The cognitive sciences have clariﬁed the different forms and functions of attention [GAO 06, p. e. preparing, alternating, supervising). Providing an MMD with such abilities and limits only makes sense if you are trying to reproduce human fallibility. If on the contrary, you are trying to design an understanding, performing system able to use any clue that allows it to solve the task for which it was designed, there is nothing to prevent all the recorded information to be processed, without selecting or allocating the system’s attention.
E. the clariﬁcation requests and other transient sub-dialogues, which do not inﬂuence the task’s progression but allow the interlocutors to understand each other. This dialogue structure allows the system to carry out ﬁne analysis and also to assess in real time the task’s progression, without requiring the implementation of a more complex model such as the hierarchical model of the school of Geneva [ROU 85], mentioned earlier as an approach to discourse analysis. 3. Current systems: multiplicity of ﬁelds and techniques Our overview started in the 1950s and now reaches the 2000s.
Man-Machine Dialogue by Frédéric Landragin(auth.)