Request Correction

Referring Expression Generation in time-constrained communication

In game-like applications and many others, an underlying Natural Language Generation system may have to express urgency or other dynamic aspects of a fast-evolving situation as text, which may be considerably different from text produced under so-called `normal' circumstances (e.g., without time constrains). As a means to shed light on possible differences of this kind, this paper addresses the computational generation of natural language text in time-constrained communication by presenting two experiments that use the attribute selection task of definite descriptions (or Referring Expression Generation - REG) as a working example. In the first experiment, we describe a psycholinguistic study in which human participants are engaged in a time-constrained reference production task. This results in a corpus of time-constrained descriptions to be compared with `normal' descriptions available from an existing (i.e., with no time constraint) REG corpus. In the second experiment, we discuss how a REG algorithm may be customised so as to produce time-constrained descriptions that resemble those produced by human speakers in similar situations. The proposed algorithm is then evaluated against the time-constrained descriptions produced by the human subjects in the first experiment, and it is shown to outperform standard approaches to REG in these conditions.