@inproceedings{kann-etal-2017-neural,
title = "Neural Multi-Source Morphological Reinflection",
author = {Kann, Katharina and
Cotterell, Ryan and
Sch{\"u}tze, Hinrich},
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-1049/",
pages = "514--524",
abstract = "We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version. The input consists of (i) a target tag and (ii) multiple pairs of source form and source tag for a lemma. The motivation is that it is beneficial to have access to more than one source form since different source forms can provide complementary information, e.g., different stems. We further present a novel extension to the encoder-decoder recurrent neural architecture, consisting of multiple encoders, to better solve the task. We show that our new architecture outperforms single-source reinflection models and publish our dataset for multi-source morphological reinflection to facilitate future research."
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%0 Conference Proceedings
%T Neural Multi-Source Morphological Reinflection
%A Kann, Katharina
%A Cotterell, Ryan
%A Schütze, Hinrich
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F kann-etal-2017-neural
%X We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version. The input consists of (i) a target tag and (ii) multiple pairs of source form and source tag for a lemma. The motivation is that it is beneficial to have access to more than one source form since different source forms can provide complementary information, e.g., different stems. We further present a novel extension to the encoder-decoder recurrent neural architecture, consisting of multiple encoders, to better solve the task. We show that our new architecture outperforms single-source reinflection models and publish our dataset for multi-source morphological reinflection to facilitate future research.
%U https://aclanthology.org/E17-1049/
%P 514-524
Markdown (Informal)
[Neural Multi-Source Morphological Reinflection](https://aclanthology.org/E17-1049/) (Kann et al., EACL 2017)
ACL
- Katharina Kann, Ryan Cotterell, and Hinrich Schütze. 2017. Neural Multi-Source Morphological Reinflection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 514–524, Valencia, Spain. Association for Computational Linguistics.