@inproceedings{liu-hulden-2020-analogy,
title = "Analogy Models for Neural Word Inflection",
author = "Liu, Ling and
Hulden, Mans",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.257",
doi = "10.18653/v1/2020.coling-main.257",
pages = "2861--2878",
abstract = "Analogy is assumed to be the cognitive mechanism speakers resort to in order to inflect an unknown form of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but also across paradigms. As neural network models for inflection are typically trained only on lemma-target form pairs, we propose three new ways to provide neural models with additional source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature. We show that the proposed methods of providing a Transformer sequence-to-sequence model with additional analogy sources in the input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We also propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the training data is extremely limited.",
}
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%0 Conference Proceedings
%T Analogy Models for Neural Word Inflection
%A Liu, Ling
%A Hulden, Mans
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F liu-hulden-2020-analogy
%X Analogy is assumed to be the cognitive mechanism speakers resort to in order to inflect an unknown form of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but also across paradigms. As neural network models for inflection are typically trained only on lemma-target form pairs, we propose three new ways to provide neural models with additional source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature. We show that the proposed methods of providing a Transformer sequence-to-sequence model with additional analogy sources in the input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We also propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the training data is extremely limited.
%R 10.18653/v1/2020.coling-main.257
%U https://aclanthology.org/2020.coling-main.257
%U https://doi.org/10.18653/v1/2020.coling-main.257
%P 2861-2878
Markdown (Informal)
[Analogy Models for Neural Word Inflection](https://aclanthology.org/2020.coling-main.257) (Liu & Hulden, COLING 2020)
ACL
- Ling Liu and Mans Hulden. 2020. Analogy Models for Neural Word Inflection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2861–2878, Barcelona, Spain (Online). International Committee on Computational Linguistics.