Language transfer learning for supervised lexical substitution

18Citations
Citations of this article
112Readers
Mendeley users who have this article in their library.

Abstract

We propose a framework for lexical substitution that is able to perform transfer learning across languages. Datasets for this task are available in at least three languages (English, Italian, and German). Previous work has addressed each of these tasks in isolation. In contrast, we regard the union of three shared tasks as a combined multilingual dataset. We show that a supervised system can be trained effectively, even if training and evaluation data are from different languages. Successful transfer learning between languages suggests that the learned model is in fact independent of the underlying language. We combine state-of-the-art unsupervised features obtained from syntactic word embeddings and distributional thesauri in a supervised delexicalized ranking system. Our system improves over state of the art in the full lexical substitution task in all three languages.

Cite

CITATION STYLE

APA

Hintz, G., & Biemann, C. (2016). Language transfer learning for supervised lexical substitution. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (Vol. 1, pp. 118–129). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p16-1012

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free