DeepAI AI Chat
Log In Sign Up

Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021

by   Karthik Puranik, et al.
SSN Institutions
Insight Centre for Data Analytics

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English->Marathi and English->Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English->Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English->Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English->Marathi, Irish->English, and English->Irish, respectively.


page 1

page 2

page 3

page 4


The RGNLP Machine Translation Systems for WAT 2018

This paper presents the system description of Machine Translation (MT) s...

Finetuning a Kalaallisut-English machine translation system using web-crawled data

West Greenlandic, known by native speakers as Kalaallisut, is an extreme...

Netmarble AI Center's WMT21 Automatic Post-Editing Shared Task Submission

This paper describes Netmarble's submission to WMT21 Automatic Post-Edit...

MATra: A Multilingual Attentive Transliteration System for Indian Scripts

Transliteration is a task in the domain of NLP where the output word is ...

Facilitating Global Team Meetings Between Language-Based Subgroups: When and How Can Machine Translation Help?

Global teams frequently consist of language-based subgroups who put toge...

Adaptive Machine Translation with Large Language Models

Consistency is a key requirement of high-quality translation. It is espe...

LEPOR: An Augmented Machine Translation Evaluation Metric

Machine translation (MT) was developed as one of the hottest research to...