Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but compose all phrases in the same way, or they perform word-specific compositions at the cost of a far larger number of parameters. In this paper we propose transformation weighting (TransWeight), a composition model that consistently outperforms existing models on nominal compounds, adjective-noun phrases, and adverb-adjective phrases in English, German, and Dutch. TransWeight drastically reduces the number of parameters needed compared with the best model in the literature by composing similar words in the same way.
%0 Journal Article
%1 dima-etal-2019-word
%A Dima, Corina
%A de Kok, Daniël
%A Witte, Neele
%A Hinrichs, Erhard
%C Cambridge, MA
%D 2019
%E Lee, Lillian
%E Johnson, Mark
%E Roark, Brian
%E Nenkova, Ani
%I MIT Press
%J Transactions of the Association for Computational Linguistics
%K imported myown
%P 437--451
%R 10.1162/tacl_a_00275
%T No Word is an Island---A Transformation Weighting Model for Semantic Composition
%U https://aclanthology.org/Q19-1025
%V 7
%X Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but compose all phrases in the same way, or they perform word-specific compositions at the cost of a far larger number of parameters. In this paper we propose transformation weighting (TransWeight), a composition model that consistently outperforms existing models on nominal compounds, adjective-noun phrases, and adverb-adjective phrases in English, German, and Dutch. TransWeight drastically reduces the number of parameters needed compared with the best model in the literature by composing similar words in the same way.
@article{dima-etal-2019-word,
abstract = {Composition models of distributional semantics are used to construct phrase representations from the representations of their words. Composition models are typically situated on two ends of a spectrum. They either have a small number of parameters but compose all phrases in the same way, or they perform word-specific compositions at the cost of a far larger number of parameters. In this paper we propose transformation weighting (TransWeight), a composition model that consistently outperforms existing models on nominal compounds, adjective-noun phrases, and adverb-adjective phrases in English, German, and Dutch. TransWeight drastically reduces the number of parameters needed compared with the best model in the literature by composing similar words in the same way.},
added-at = {2023-11-27T12:11:53.000+0100},
address = {Cambridge, MA},
author = {Dima, Corina and de Kok, Dani{\"e}l and Witte, Neele and Hinrichs, Erhard},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2f6b82209dea985a1a0ec0d7ebf510c4c/gdima},
doi = {10.1162/tacl_a_00275},
editor = {Lee, Lillian and Johnson, Mark and Roark, Brian and Nenkova, Ani},
interhash = {64a5cf1d9d233f80691c5084ca1660c5},
intrahash = {f6b82209dea985a1a0ec0d7ebf510c4c},
journal = {Transactions of the Association for Computational Linguistics},
keywords = {imported myown},
pages = {437--451},
publisher = {MIT Press},
timestamp = {2023-11-27T12:16:40.000+0100},
title = {No Word is an {I}sland{---}{A} Transformation Weighting Model for Semantic Composition},
url = {https://aclanthology.org/Q19-1025},
volume = 7,
year = 2019
}
