@inproceedings{tula-etal-2021-bitions,
title = "Bitions@{D}ravidian{L}ang{T}ech-{EACL}2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in {D}ravidian Languages",
author = "Tula, Debapriya and
Potluri, Prathyush and
Ms, Shreyas and
Doddapaneni, Sumanth and
Sahu, Pranjal and
Sukumaran, Rohan and
Patwa, Parth",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Kumar M, Anand and
Krishnamurthy, Parameswari and
Sherly, Elizabeth",
booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
month = apr,
year = "2021",
address = "Kyiv",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.dravidianlangtech-1.42",
pages = "291--299",
abstract = "With the advent of social media, we have seen a proliferation of data and public discourse. Unfortunately, this includes offensive content as well. The problem is exacerbated due to the sheer number of languages spoken on these platforms and the multiple other modalities used for sharing offensive content (images, gifs, videos and more). In this paper, we propose a multilingual ensemble-based model that can identify offensive content targeted against an individual (or group) in low resource Dravidian language. Our model is able to handle code-mixed data as well as instances where the script used is mixed (for instance, Tamil and Latin). Our solution ranked number one for the Malayalam dataset and ranked 4th and 5th for Tamil and Kannada, respectively.",
}
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%0 Conference Proceedings
%T Bitions@DravidianLangTech-EACL2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages
%A Tula, Debapriya
%A Potluri, Prathyush
%A Ms, Shreyas
%A Doddapaneni, Sumanth
%A Sahu, Pranjal
%A Sukumaran, Rohan
%A Patwa, Parth
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Kumar M, Anand
%Y Krishnamurthy, Parameswari
%Y Sherly, Elizabeth
%S Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages
%D 2021
%8 April
%I Association for Computational Linguistics
%C Kyiv
%F tula-etal-2021-bitions
%X With the advent of social media, we have seen a proliferation of data and public discourse. Unfortunately, this includes offensive content as well. The problem is exacerbated due to the sheer number of languages spoken on these platforms and the multiple other modalities used for sharing offensive content (images, gifs, videos and more). In this paper, we propose a multilingual ensemble-based model that can identify offensive content targeted against an individual (or group) in low resource Dravidian language. Our model is able to handle code-mixed data as well as instances where the script used is mixed (for instance, Tamil and Latin). Our solution ranked number one for the Malayalam dataset and ranked 4th and 5th for Tamil and Kannada, respectively.
%U https://aclanthology.org/2021.dravidianlangtech-1.42
%P 291-299
Markdown (Informal)
[Bitions@DravidianLangTech-EACL2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages](https://aclanthology.org/2021.dravidianlangtech-1.42) (Tula et al., DravidianLangTech 2021)
ACL