@inproceedings{vaduguru-etal-2021-stress,
title = "Stress Rules from Surface Forms: Experiments with Program Synthesis",
author = "Vaduguru, Saujas and
Sarthi, Partho and
Choudhury, Monojit and
Sharma, Dipti",
editor = "Bandyopadhyay, Sivaji and
Devi, Sobha Lalitha and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2021",
address = "National Institute of Technology Silchar, Silchar, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-main.76/",
pages = "619--628",
abstract = "Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis {--} a method to learn rules from data in the form of programs in a domain-specific language {--} can be used to learn phonological rules in highly data-constrained settings. In this paper, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalization ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learnt from a small number of examples."
}
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<abstract>Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis – a method to learn rules from data in the form of programs in a domain-specific language – can be used to learn phonological rules in highly data-constrained settings. In this paper, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalization ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learnt from a small number of examples.</abstract>
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%0 Conference Proceedings
%T Stress Rules from Surface Forms: Experiments with Program Synthesis
%A Vaduguru, Saujas
%A Sarthi, Partho
%A Choudhury, Monojit
%A Sharma, Dipti
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F vaduguru-etal-2021-stress
%X Learning linguistic generalizations from only a few examples is a challenging task. Recent work has shown that program synthesis – a method to learn rules from data in the form of programs in a domain-specific language – can be used to learn phonological rules in highly data-constrained settings. In this paper, we use the problem of phonological stress placement as a case to study how the design of the domain-specific language influences the generalization ability when using the same learning algorithm. We find that encoding the distinction between consonants and vowels results in much better performance, and providing syllable-level information further improves generalization. Program synthesis, thus, provides a way to investigate how access to explicit linguistic information influences what can be learnt from a small number of examples.
%U https://aclanthology.org/2021.icon-main.76/
%P 619-628
Markdown (Informal)
[Stress Rules from Surface Forms: Experiments with Program Synthesis](https://aclanthology.org/2021.icon-main.76/) (Vaduguru et al., ICON 2021)
ACL
- Saujas Vaduguru, Partho Sarthi, Monojit Choudhury, and Dipti Sharma. 2021. Stress Rules from Surface Forms: Experiments with Program Synthesis. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 619–628, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).