Inflection Generation for Spanish Verbs using Supervised Learning

Cristina Barros, Dimitra Gkatzia, Elena Lloret


Abstract
We present a novel supervised approach to inflection generation for verbs in Spanish. Our system takes as input the verb’s lemma form and the desired features such as person, number, tense, and is able to predict the appropriate grammatical conjugation. Even though our approach learns from fewer examples comparing to previous work, it is able to deal with all the Spanish moods (indicative, subjunctive and imperative) in contrast to previous work which only focuses on indicative and subjunctive moods. We show that in an intrinsic evaluation, our system achieves 99% accuracy, outperforming (although not significantly) two competitive state-of-art systems. The successful results obtained clearly indicate that our approach could be integrated into wider approaches related to text generation in Spanish.
Anthology ID:
W17-4120
Volume:
Proceedings of the First Workshop on Subword and Character Level Models in NLP
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
SCLeM
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–141
Language:
URL:
https://aclanthology.org/W17-4120
DOI:
10.18653/v1/W17-4120
Bibkey:
Cite (ACL):
Cristina Barros, Dimitra Gkatzia, and Elena Lloret. 2017. Inflection Generation for Spanish Verbs using Supervised Learning. In Proceedings of the First Workshop on Subword and Character Level Models in NLP, pages 136–141, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Inflection Generation for Spanish Verbs using Supervised Learning (Barros et al., SCLeM 2017)
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PDF:
https://aclanthology.org/W17-4120.pdf