Improving the generation of personalised descriptions

WS 2017  ·  Thiago Castro Ferreira, Iv Paraboni, r{\'e} ·

Referring expression generation (REG) models that use speaker-dependent information require a considerable amount of training data produced by every individual speaker, or may otherwise perform poorly. In this work we propose a simple personalised method for this task, in which speakers are grouped into profiles according to their referential behaviour... Intrinsic evaluation shows that the use of speaker{'}s profiles generally outperforms the personalised method found in previous work. read more

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