DeepAI AI Chat
Log In Sign Up

Multitask Finetuning for Improving Neural Machine Translation in Indian Languages

by   Shaily Desai, et al.

Transformer based language models have led to impressive results across all domains in Natural Language Processing. Pretraining these models on language modeling tasks and finetuning them on downstream tasks such as Text Classification, Question Answering and Neural Machine Translation has consistently shown exemplary results. In this work, we propose a Multitask Finetuning methodology which combines the Bilingual Machine Translation task with an auxiliary Causal Language Modeling task to improve performance on the former task on Indian Languages. We conduct an empirical study on three language pairs, Marathi-Hindi, Marathi-English and Hindi-English, where we compare the multitask finetuning approach to the standard finetuning approach, for which we use the mBART50 model. Our study indicates that the multitask finetuning method could be a better technique than standard finetuning, and could improve Bilingual Machine Translation across language pairs.


page 1

page 2

page 3

page 4


Towards Machine Translation for the Kurdish Language

Machine translation is the task of translating texts from one language t...

Reducing Transformer Depth on Demand with Structured Dropout

Overparameterized transformer networks have obtained state of the art re...

Inducing Constituency Trees through Neural Machine Translation

Latent tree learning(LTL) methods learn to parse sentences using only in...

The boundaries of meaning: a case study in neural machine translation

The success of deep learning in natural language processing raises intri...

How Good are Commercial Large Language Models on African Languages?

Recent advancements in Natural Language Processing (NLP) has led to the ...

An Empirical Study of Generation Order for Machine Translation

In this work, we present an empirical study of generation order for mach...

Interpreting Verbal Metaphors by Paraphrasing

Metaphorical expressions are difficult linguistic phenomena, challenging...