@inproceedings{eibers-etal-2023-improving,
title = "Improving Low-resource {RRG} Parsing with Structured Gloss Embeddings",
author = "Eibers, Roland and
Evang, Kilian and
Kallmeyer, Laura",
editor = "Serikov, Oleg and
Voloshina, Ekaterina and
Postnikova, Anna and
Klyachko, Elena and
Vylomova, Ekaterina and
Shavrina, Tatiana and
Le Ferrand, Eric and
Malykh, Valentin and
Tyers, Francis and
Arkhangelskiy, Timofey and
Mikhailov, Vladislav",
booktitle = "Proceedings of the Second Workshop on NLP Applications to Field Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.fieldmatters-1.6",
doi = "10.18653/v1/2023.fieldmatters-1.6",
pages = "46--51",
abstract = "Treebanking for local languages is hampered by the lack of existing parsers to generate pre-annotations. However, it has been shown that reasonably accurate parsers can be bootstrapped with little initial training data when use is made of the information in interlinear glosses and translations that language documentation data for such treebanks typically comes with. In this paper, we improve upon such a bootstrapping model by representing glosses using a combination of morphological feature vectors and pre-trained lemma embeddings. We also contribute a mapping from glosses to Universal Dependencies morphological features.",
}
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<abstract>Treebanking for local languages is hampered by the lack of existing parsers to generate pre-annotations. However, it has been shown that reasonably accurate parsers can be bootstrapped with little initial training data when use is made of the information in interlinear glosses and translations that language documentation data for such treebanks typically comes with. In this paper, we improve upon such a bootstrapping model by representing glosses using a combination of morphological feature vectors and pre-trained lemma embeddings. We also contribute a mapping from glosses to Universal Dependencies morphological features.</abstract>
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%0 Conference Proceedings
%T Improving Low-resource RRG Parsing with Structured Gloss Embeddings
%A Eibers, Roland
%A Evang, Kilian
%A Kallmeyer, Laura
%Y Serikov, Oleg
%Y Voloshina, Ekaterina
%Y Postnikova, Anna
%Y Klyachko, Elena
%Y Vylomova, Ekaterina
%Y Shavrina, Tatiana
%Y Le Ferrand, Eric
%Y Malykh, Valentin
%Y Tyers, Francis
%Y Arkhangelskiy, Timofey
%Y Mikhailov, Vladislav
%S Proceedings of the Second Workshop on NLP Applications to Field Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F eibers-etal-2023-improving
%X Treebanking for local languages is hampered by the lack of existing parsers to generate pre-annotations. However, it has been shown that reasonably accurate parsers can be bootstrapped with little initial training data when use is made of the information in interlinear glosses and translations that language documentation data for such treebanks typically comes with. In this paper, we improve upon such a bootstrapping model by representing glosses using a combination of morphological feature vectors and pre-trained lemma embeddings. We also contribute a mapping from glosses to Universal Dependencies morphological features.
%R 10.18653/v1/2023.fieldmatters-1.6
%U https://aclanthology.org/2023.fieldmatters-1.6
%U https://doi.org/10.18653/v1/2023.fieldmatters-1.6
%P 46-51
Markdown (Informal)
[Improving Low-resource RRG Parsing with Structured Gloss Embeddings](https://aclanthology.org/2023.fieldmatters-1.6) (Eibers et al., FieldMatters 2023)
ACL