@inproceedings{srinivasan-etal-2020-code,
title = "Code-mixed parse trees and how to find them",
author = "Srinivasan, Anirudh and
Dandapat, Sandipan and
Choudhury, Monojit",
editor = "Solorio, Thamar and
Choudhury, Monojit and
Bali, Kalika and
Sitaram, Sunayana and
Das, Amitava and
Diab, Mona",
booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Code Switching",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.calcs-1.8",
pages = "57--64",
abstract = "In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into {``}pseudo constituency trees{''} and find that a parser trained on synthetically generated trees is able to decently parse these as well.",
language = "English",
ISBN = "979-10-95546-66-5",
}
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%0 Conference Proceedings
%T Code-mixed parse trees and how to find them
%A Srinivasan, Anirudh
%A Dandapat, Sandipan
%A Choudhury, Monojit
%Y Solorio, Thamar
%Y Choudhury, Monojit
%Y Bali, Kalika
%Y Sitaram, Sunayana
%Y Das, Amitava
%Y Diab, Mona
%S Proceedings of the 4th Workshop on Computational Approaches to Code Switching
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-66-5
%G English
%F srinivasan-etal-2020-code
%X In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into “pseudo constituency trees” and find that a parser trained on synthetically generated trees is able to decently parse these as well.
%U https://aclanthology.org/2020.calcs-1.8
%P 57-64
Markdown (Informal)
[Code-mixed parse trees and how to find them](https://aclanthology.org/2020.calcs-1.8) (Srinivasan et al., CALCS 2020)
ACL
- Anirudh Srinivasan, Sandipan Dandapat, and Monojit Choudhury. 2020. Code-mixed parse trees and how to find them. In Proceedings of the 4th Workshop on Computational Approaches to Code Switching, pages 57–64, Marseille, France. European Language Resources Association.