@inproceedings{fridriksdottir-etal-2022-icebats,
title = "{I}ce{BATS}: An {I}celandic Adaptation of the Bigger Analogy Test Set",
author = "Fri{\dh}riksd{\'o}ttir, Steinunn Rut and
Dan{\'\i}elsson, Hjalti and
Steingr{\'\i}msson, Stein{\th}{\'o}r and
Sigurdsson, Einar",
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
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.449",
pages = "4227--4234",
abstract = "Word embedding models have become commonplace in a wide range of NLP applications. In order to train and use the best possible models, accurate evaluation is needed. For extrinsic evaluation of word embedding models, analogy evaluation sets have been shown to be a good quality estimator. We introduce an Icelandic adaptation of a large analogy dataset, BATS, evaluate it on three different word embedding models and show that our evaluation set is apt at measuring the capabilities of such models.",
}
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%0 Conference Proceedings
%T IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set
%A Fri\dhriksdóttir, Steinunn Rut
%A Daníelsson, Hjalti
%A Steingrímsson, Stein\thór
%A Sigurdsson, Einar
%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 Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F fridriksdottir-etal-2022-icebats
%X Word embedding models have become commonplace in a wide range of NLP applications. In order to train and use the best possible models, accurate evaluation is needed. For extrinsic evaluation of word embedding models, analogy evaluation sets have been shown to be a good quality estimator. We introduce an Icelandic adaptation of a large analogy dataset, BATS, evaluate it on three different word embedding models and show that our evaluation set is apt at measuring the capabilities of such models.
%U https://aclanthology.org/2022.lrec-1.449
%P 4227-4234
Markdown (Informal)
[IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set](https://aclanthology.org/2022.lrec-1.449) (Friðriksdóttir et al., LREC 2022)
ACL
- Steinunn Rut Friðriksdóttir, Hjalti Daníelsson, Steinþór Steingrímsson, and Einar Sigurdsson. 2022. IceBATS: An Icelandic Adaptation of the Bigger Analogy Test Set. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4227–4234, Marseille, France. European Language Resources Association.