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.

翻译：基于变换的语文模型在自然语言处理的所有领域都取得了令人印象深刻的成果,在语言模拟任务方面对这些模型进行预先培训,并在下游任务(如文本分类、问答和神经机器翻译)上对其进行微调,这些模型始终显示出堪称典范的结果。在这项工作中,我们提出了多任务微调方法,将双语机器翻译任务与辅助性因果语言模型任务结合起来,以提高以前印度语言任务的业绩。我们就三种语文对(Marathi-Hindi、Marathi-Engli和Hindi-Engli)进行了实证研究,将多任务微调方法与标准微调方法(我们为此使用 mBART50模型)进行比较。我们的研究显示,多任务微调方法比标准微调更好,可以改进跨语言对口双语机器翻译。