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.
@InProceedings{MARIOTTI18.39, author = {André Mariotti and Ivandré Paraboni}, title = "{Referring Expression Generation in time-constrained communication}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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