@inproceedings{bhathena-etal-2020-evaluating,
title = "Evaluating Compositionality of Sentence Representation Models",
author = "Bhathena, Hanoz and
Willis, Angelica and
Dass, Nathan",
editor = "Gella, Spandana and
Welbl, Johannes and
Rei, Marek and
Petroni, Fabio and
Lewis, Patrick and
Strubell, Emma and
Seo, Minjoon and
Hajishirzi, Hannaneh",
booktitle = "Proceedings of the 5th Workshop on Representation Learning for NLP",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.repl4nlp-1.22",
doi = "10.18653/v1/2020.repl4nlp-1.22",
pages = "185--193",
abstract = "We evaluate the compositionality of general-purpose sentence encoders by proposing two different metrics to quantify compositional understanding capability of sentence encoders. We introduce a novel metric, Polarity Sensitivity Scoring (PSS), which utilizes sentiment perturbations as a proxy for measuring compositionality. We then compare results from PSS with those obtained via our proposed extension of a metric called Tree Reconstruction Error (TRE) (CITATION) where compositionality is evaluated by measuring how well a true representation producing model can be approximated by a model that explicitly combines representations of its primitives.",
}
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%0 Conference Proceedings
%T Evaluating Compositionality of Sentence Representation Models
%A Bhathena, Hanoz
%A Willis, Angelica
%A Dass, Nathan
%Y Gella, Spandana
%Y Welbl, Johannes
%Y Rei, Marek
%Y Petroni, Fabio
%Y Lewis, Patrick
%Y Strubell, Emma
%Y Seo, Minjoon
%Y Hajishirzi, Hannaneh
%S Proceedings of the 5th Workshop on Representation Learning for NLP
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F bhathena-etal-2020-evaluating
%X We evaluate the compositionality of general-purpose sentence encoders by proposing two different metrics to quantify compositional understanding capability of sentence encoders. We introduce a novel metric, Polarity Sensitivity Scoring (PSS), which utilizes sentiment perturbations as a proxy for measuring compositionality. We then compare results from PSS with those obtained via our proposed extension of a metric called Tree Reconstruction Error (TRE) (CITATION) where compositionality is evaluated by measuring how well a true representation producing model can be approximated by a model that explicitly combines representations of its primitives.
%R 10.18653/v1/2020.repl4nlp-1.22
%U https://aclanthology.org/2020.repl4nlp-1.22
%U https://doi.org/10.18653/v1/2020.repl4nlp-1.22
%P 185-193
Markdown (Informal)
[Evaluating Compositionality of Sentence Representation Models](https://aclanthology.org/2020.repl4nlp-1.22) (Bhathena et al., RepL4NLP 2020)
ACL