@inproceedings{chaudhary-etal-2020-automatic,
title = "Automatic Extraction of Rules Governing Morphological Agreement",
author = "Chaudhary, Aditi and
Anastasopoulos, Antonios and
Pratapa, Adithya and
Mortensen, David R. and
Sheikh, Zaid and
Tsvetkov, Yulia and
Neubig, Graham",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.422",
doi = "10.18653/v1/2020.emnlp-main.422",
pages = "5212--5236",
abstract = "Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world{'}s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78{\%}. We release an interface demonstrating the extracted rules at \url{https://neulab.github.io/lase/}",
}
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<abstract>Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world’s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78%. We release an interface demonstrating the extracted rules at https://neulab.github.io/lase/</abstract>
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%0 Conference Proceedings
%T Automatic Extraction of Rules Governing Morphological Agreement
%A Chaudhary, Aditi
%A Anastasopoulos, Antonios
%A Pratapa, Adithya
%A Mortensen, David R.
%A Sheikh, Zaid
%A Tsvetkov, Yulia
%A Neubig, Graham
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chaudhary-etal-2020-automatic
%X Creating a descriptive grammar of a language is an indispensable step for language documentation and preservation. However, at the same time it is a tedious, time-consuming task. In this paper, we take steps towards automating this process by devising an automated framework for extracting a first-pass grammatical specification from raw text in a concise, human- and machine-readable format. We focus on extracting rules describing agreement, a morphosyntactic phenomenon at the core of the grammars of many of the world’s languages. We apply our framework to all languages included in the Universal Dependencies project, with promising results. Using cross-lingual transfer, even with no expert annotations in the language of interest, our framework extracts a grammatical specification which is nearly equivalent to those created with large amounts of gold-standard annotated data. We confirm this finding with human expert evaluations of the rules that our framework produces, which have an average accuracy of 78%. We release an interface demonstrating the extracted rules at https://neulab.github.io/lase/
%R 10.18653/v1/2020.emnlp-main.422
%U https://aclanthology.org/2020.emnlp-main.422
%U https://doi.org/10.18653/v1/2020.emnlp-main.422
%P 5212-5236
Markdown (Informal)
[Automatic Extraction of Rules Governing Morphological Agreement](https://aclanthology.org/2020.emnlp-main.422) (Chaudhary et al., EMNLP 2020)
ACL
- Aditi Chaudhary, Antonios Anastasopoulos, Adithya Pratapa, David R. Mortensen, Zaid Sheikh, Yulia Tsvetkov, and Graham Neubig. 2020. Automatic Extraction of Rules Governing Morphological Agreement. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5212–5236, Online. Association for Computational Linguistics.