@inproceedings{kann-etal-2017-one,
title = "One-Shot Neural Cross-Lingual Transfer for Paradigm Completion",
author = {Kann, Katharina and
Cotterell, Ryan and
Sch{\"u}tze, Hinrich},
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1182",
doi = "10.18653/v1/P17-1182",
pages = "1993--2003",
abstract = "We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58{\%} higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.",
}
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%0 Conference Proceedings
%T One-Shot Neural Cross-Lingual Transfer for Paradigm Completion
%A Kann, Katharina
%A Cotterell, Ryan
%A Schütze, Hinrich
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F kann-etal-2017-one
%X We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58% higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.
%R 10.18653/v1/P17-1182
%U https://aclanthology.org/P17-1182
%U https://doi.org/10.18653/v1/P17-1182
%P 1993-2003
Markdown (Informal)
[One-Shot Neural Cross-Lingual Transfer for Paradigm Completion](https://aclanthology.org/P17-1182) (Kann et al., ACL 2017)
ACL
- Katharina Kann, Ryan Cotterell, and Hinrich Schütze. 2017. One-Shot Neural Cross-Lingual Transfer for Paradigm Completion. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1993–2003, Vancouver, Canada. Association for Computational Linguistics.