Gated Convolutional Sequence to Sequence Based Learning for English-Hingilsh Code-Switched Machine Translation.

Suman Dowlagar, Radhika Mamidi


Abstract
Code-Switching is the embedding of linguistic units or phrases from two or more languages in a single sentence. This phenomenon is practiced in all multilingual communities and is prominent in social media. Consequently, there is a growing need to understand code-switched translations by translating the code-switched text into one of the standard languages or vice versa. Neural Machine translation is a well-studied research problem in the monolingual text. In this paper, we have used the gated convolutional sequences to sequence networks for English-Hinglish translation. The convolutions in the model help to identify the compositional structure in the sequences more easily. The model relies on gating and performs multiple attention steps at encoder and decoder layers.
Anthology ID:
2021.calcs-1.4
Volume:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:
June
Year:
2021
Address:
Online
Venues:
CALCS | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–30
Language:
URL:
https://aclanthology.org/2021.calcs-1.4
DOI:
10.18653/v1/2021.calcs-1.4
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.4.pdf