@inproceedings{oliver-etal-2022-inflectional,
title = "An Inflectional Database for Gitksan",
author = "Oliver, Bruce and
Forbes, Clarissa and
Yang, Changbing and
Samir, Farhan and
Coates, Edith and
Nicolai, Garrett and
Silfverberg, Miikka",
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-1.710",
pages = "6597--6606",
abstract = "This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.",
}
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<abstract>This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.</abstract>
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%0 Conference Proceedings
%T An Inflectional Database for Gitksan
%A Oliver, Bruce
%A Forbes, Clarissa
%A Yang, Changbing
%A Samir, Farhan
%A Coates, Edith
%A Nicolai, Garrett
%A Silfverberg, Miikka
%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 oliver-etal-2022-inflectional
%X This paper presents a new inflectional resource for Gitksan, a low-resource Indigenous language of Canada. We use Gitksan data in interlinear glossed format, stemming from language documentation efforts, to build a database of partial inflection tables. We then enrich this morphological resource by filling in blank slots in the partial inflection tables using neural transformer reinflection models. We extend the training data for our transformer reinflection models using two data augmentation techniques: data hallucination and back-translation. Experimental results demonstrate substantial improvements from data augmentation, with data hallucination delivering particularly impressive gains. We also release reinflection models for Gitksan.
%U https://aclanthology.org/2022.lrec-1.710
%P 6597-6606
Markdown (Informal)
[An Inflectional Database for Gitksan](https://aclanthology.org/2022.lrec-1.710) (Oliver et al., LREC 2022)
ACL
- Bruce Oliver, Clarissa Forbes, Changbing Yang, Farhan Samir, Edith Coates, Garrett Nicolai, and Miikka Silfverberg. 2022. An Inflectional Database for Gitksan. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6597–6606, Marseille, France. European Language Resources Association.