Stress Rules from Surface Forms: Experiments with Program Synthesis

Saujas Vaduguru, Partho Sarthi, Monojit Choudhury, Dipti Sharma


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.
Anthology ID:
2021.icon-main.76
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
619–628
Language:
URL:
https://aclanthology.org/2021.icon-main.76
DOI:
Bibkey:
Cite (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).
Cite (Informal):
Stress Rules from Surface Forms: Experiments with Program Synthesis (Vaduguru et al., ICON 2021)
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https://aclanthology.org/2021.icon-main.76.pdf
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