Improving the Naturalness and Expressivity of Language Generation for Spanish

Cristina Barros, Dimitra Gkatzia, Elena Lloret

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Abstract
We present a flexible Natural Language Generation approach for Spanish, focused on the surface realisation stage, which integrates an inflection module in order to improve the naturalness and expressivity of the generated language. This inflection module inflects the verbs using an ensemble of trainable algorithms whereas the other types of words (e.g. nouns, determiners, etc) are inflected using hand-crafted rules. We show that our approach achieves 2% higher accuracy than two state-of-art inflection generation approaches. Furthermore, our proposed approach also predicts an extra feature: the inflection of the imperative mood, which was not taken into account by previous work. We also present a user evaluation, where we demonstrate that the proposed method significantly improves the perceived naturalness of the generated language.
Anthology ID:
W17-3505
Volume:
Proceedings of the 10th International Conference on Natural Language Generation
Month:
September
Year:
2017
Address:
Santiago de Compostela, Spain
Editors:
Jose M. Alonso, Alberto Bugarín, Ehud Reiter
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
41–50
Language:
URL:
https://aclanthology.org/W17-3505
DOI:
10.18653/v1/W17-3505
Bibkey:
Cite (ACL):
Cristina Barros, Dimitra Gkatzia, and Elena Lloret. 2017. Improving the Naturalness and Expressivity of Language Generation for Spanish. In Proceedings of the 10th International Conference on Natural Language Generation, pages 41–50, Santiago de Compostela, Spain. Association for Computational Linguistics.
Cite (Informal):
Improving the Naturalness and Expressivity of Language Generation for Spanish (Barros et al., INLG 2017)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W17-3505.pdf
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