@inproceedings{rivas-2019-modeling,
title = "Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models",
author = "Rivas, Pablo",
editor = "Axelrod, Amittai and
Yang, Diyi and
Cunha, Rossana and
Shaikh, Samira and
Waseem, Zeerak",
booktitle = "Proceedings of the 2019 Workshop on Widening NLP",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3610",
pages = "24--26",
abstract = "We present a study in which we train neural models that approximate rules that assess the quality of English sentences. We modeled five rules using deep LSTMs trained over a dataset of sentences whose quality is evaluated under such rules. Preliminary results suggest the neural architecture can model such rules to high accuracy.",
}
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%0 Conference Proceedings
%T Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models
%A Rivas, Pablo
%Y Axelrod, Amittai
%Y Yang, Diyi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Waseem, Zeerak
%S Proceedings of the 2019 Workshop on Widening NLP
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F rivas-2019-modeling
%X We present a study in which we train neural models that approximate rules that assess the quality of English sentences. We modeled five rules using deep LSTMs trained over a dataset of sentences whose quality is evaluated under such rules. Preliminary results suggest the neural architecture can model such rules to high accuracy.
%U https://aclanthology.org/W19-3610
%P 24-26
Markdown (Informal)
[Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models](https://aclanthology.org/W19-3610) (Rivas, WiNLP 2019)
ACL