@inproceedings{mazzei-basile-2019-dipinfounito,
title = "The {D}ip{I}nfo{U}ni{T}o Realizer at {SRST}{'}19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization",
author = "Mazzei, Alessandro and
Basile, Valerio",
editor = "Mille, Simon and
Belz, Anja and
Bohnet, Bernd and
Graham, Yvette and
Wanner, Leo",
booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6311",
doi = "10.18653/v1/D19-6311",
pages = "81--87",
abstract = "We describe the system presented at the SR{'}19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR{'}19.",
}
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%0 Conference Proceedings
%T The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
%A Mazzei, Alessandro
%A Basile, Valerio
%Y Mille, Simon
%Y Belz, Anja
%Y Bohnet, Bernd
%Y Graham, Yvette
%Y Wanner, Leo
%S Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mazzei-basile-2019-dipinfounito
%X We describe the system presented at the SR’19 shared task by the DipInfoUnito team. Our approach is based on supervised machine learning. In particular, we divide the SR task into two independent subtasks, namely word order prediction and morphology inflection prediction. Two neural networks with different architectures run on the same input structure, each producing a partial output which is recombined in the final step in order to produce the predicted surface form. This work is a direct successor of the architecture presented at SR’19.
%R 10.18653/v1/D19-6311
%U https://aclanthology.org/D19-6311
%U https://doi.org/10.18653/v1/D19-6311
%P 81-87
Markdown (Informal)
[The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization](https://aclanthology.org/D19-6311) (Mazzei & Basile, 2019)
ACL