IndicBART: A Pre-trained Model for Indic Natural Language Generation
Raj Dabre, Himani Shrotriya, Anoop Kunchukuttan, Ratish Puduppully, Mitesh Khapra, Pratyush Kumar
Abstract
In this paper, we study pre-trained sequence-to-sequence models for a group of related languages, with a focus on Indic languages. We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English. IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages. We evaluate IndicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization. Our experiments on NMT and extreme summarization show that a model specific to related languages like IndicBART is competitive with large pre-trained models like mBART50 despite being significantly smaller. It also performs well on very low-resource translation scenarios where languages are not included in pre-training or fine-tuning. Script sharing, multilingual training, and better utilization of limited model capacity contribute to the good performance of the compact IndicBART model.
BibTeX
@inproceedings{dabre-etal-2022-indicbart,
title = "{I}ndic{BART}: A Pre-trained Model for Indic Natural Language Generation",
author = "Dabre, Raj and
Shrotriya, Himani and
Kunchukuttan, Anoop and
Puduppully, Ratish and
Khapra, Mitesh and
Kumar, Pratyush",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.145/",
doi = "10.18653/v1/2022.findings-acl.145",
pages = "1849--1863"
}