@inproceedings{barros-etal-2017-inflection,
title = "Inflection Generation for {S}panish Verbs using Supervised Learning",
author = "Barros, Cristina and
Gkatzia, Dimitra and
Lloret, Elena",
editor = "Faruqui, Manaal and
Schuetze, Hinrich and
Trancoso, Isabel and
Yaghoobzadeh, Yadollah",
booktitle = "Proceedings of the First Workshop on Subword and Character Level Models in {NLP}",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4120",
doi = "10.18653/v1/W17-4120",
pages = "136--141",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Inflection Generation for Spanish Verbs using Supervised Learning
%A Barros, Cristina
%A Gkatzia, Dimitra
%A Lloret, Elena
%Y Faruqui, Manaal
%Y Schuetze, Hinrich
%Y Trancoso, Isabel
%Y Yaghoobzadeh, Yadollah
%S Proceedings of the First Workshop on Subword and Character Level Models in NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F barros-etal-2017-inflection
%X 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.
%R 10.18653/v1/W17-4120
%U https://aclanthology.org/W17-4120
%U https://doi.org/10.18653/v1/W17-4120
%P 136-141
Markdown (Informal)
[Inflection Generation for Spanish Verbs using Supervised Learning](https://aclanthology.org/W17-4120) (Barros et al., SCLeM 2017)
ACL