@inproceedings{boyd-graber-etal-2020-evaluations,
title = "Which Evaluations Uncover Sense Representations that Actually Make Sense?",
author = "Boyd-Graber, Jordan and
Guo, Fenfei and
Findlater, Leah and
Iyyer, Mohit",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.214",
pages = "1727--1738",
abstract = "Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word{'}s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language{'}s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<namePart type="given">Sara</namePart>
<namePart type="family">Goggi</namePart>
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<namePart type="given">Hitoshi</namePart>
<namePart type="family">Isahara</namePart>
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<abstract>Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word’s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language’s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.</abstract>
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%0 Conference Proceedings
%T Which Evaluations Uncover Sense Representations that Actually Make Sense?
%A Boyd-Graber, Jordan
%A Guo, Fenfei
%A Findlater, Leah
%A Iyyer, Mohit
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F boyd-graber-etal-2020-evaluations
%X Text representations are critical for modern natural language processing. One form of text representation, sense-specific embeddings, reflect a word’s sense in a sentence better than single-prototype word embeddings tied to each type. However, existing sense representations are not uniformly better: although they work well for computer-centric evaluations, they fail for human-centric tasks like inspecting a language’s sense inventory. To expose this discrepancy, we propose a new coherence evaluation for sense embeddings. We also describe a minimal model (Gumbel Attention for Sense Induction) optimized for discovering interpretable sense representations that are more coherent than existing sense embeddings.
%U https://aclanthology.org/2020.lrec-1.214
%P 1727-1738
Markdown (Informal)
[Which Evaluations Uncover Sense Representations that Actually Make Sense?](https://aclanthology.org/2020.lrec-1.214) (Boyd-Graber et al., LREC 2020)
ACL