@inproceedings{cathcart-rama-2020-disentangling,
title = "Disentangling dialects: a neural approach to {I}ndo-{A}ryan historical phonology and subgrouping",
author = "Cathcart, Chundra and
Rama, Taraka",
editor = "Fern{\'a}ndez, Raquel and
Linzen, Tal",
booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.conll-1.50",
doi = "10.18653/v1/2020.conll-1.50",
pages = "620--630",
abstract = "This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models{'} representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.",
}
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<abstract>This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models’ representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.</abstract>
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%0 Conference Proceedings
%T Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping
%A Cathcart, Chundra
%A Rama, Taraka
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cathcart-rama-2020-disentangling
%X This paper seeks to uncover patterns of sound change across Indo-Aryan languages using an LSTM encoder-decoder architecture. We augment our models with embeddings represent-ing language ID, part of speech, and other features such as word embeddings. We find that a highly augmented model shows highest accuracy in predicting held-out forms, and investigate other properties of interest learned by our models’ representations. We outline extensions to this architecture that can better capture variation in Indo-Aryan sound change.
%R 10.18653/v1/2020.conll-1.50
%U https://aclanthology.org/2020.conll-1.50
%U https://doi.org/10.18653/v1/2020.conll-1.50
%P 620-630
Markdown (Informal)
[Disentangling dialects: a neural approach to Indo-Aryan historical phonology and subgrouping](https://aclanthology.org/2020.conll-1.50) (Cathcart & Rama, CoNLL 2020)
ACL