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    <abstract>Collecting modality exclusivity norms for lexical items has recently become a common practice in psycholinguistics and cognitive research. However, these norms are available only for a relatively small number of languages and often involve a costly and time-consuming collection of ratings. In this work, we aim at learning a mapping between word embeddings and modality norms. Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one. We ran two experiments, one in a monolingual and the other one in a crosslingual setting. Results show that modality prediction using off-the-shelf crosslingual embeddings indeed has moderate-to-high correlations with human ratings even when regression algorithms are trained on an English resource and tested on a completely unseen language.</abstract>
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%0 Conference Proceedings
%T Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings
%A Chersoni, Emmanuele
%A Xiang, Rong
%A Lu, Qin
%A Huang, Chu-Ren
%Y Gurevych, Iryna
%Y Apidianaki, Marianna
%Y Faruqui, Manaal
%S Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F chersoni-etal-2020-automatic
%X Collecting modality exclusivity norms for lexical items has recently become a common practice in psycholinguistics and cognitive research. However, these norms are available only for a relatively small number of languages and often involve a costly and time-consuming collection of ratings. In this work, we aim at learning a mapping between word embeddings and modality norms. Our experiments focused on crosslingual word embeddings, in order to predict modality association scores by training on a high-resource language and testing on a low-resource one. We ran two experiments, one in a monolingual and the other one in a crosslingual setting. Results show that modality prediction using off-the-shelf crosslingual embeddings indeed has moderate-to-high correlations with human ratings even when regression algorithms are trained on an English resource and tested on a completely unseen language.
%U https://aclanthology.org/2020.starsem-1.4
%P 32-38

Markdown (Informal)

[Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings](https://aclanthology.org/2020.starsem-1.4) (Chersoni et al., *SEM 2020)

• Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings (Chersoni et al., *SEM 2020)

ACL

• Emmanuele Chersoni, Rong Xiang, Qin Lu, and Chu-Ren Huang. 2020. Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 32–38, Barcelona, Spain (Online). Association for Computational Linguistics.