The ADAPT System Description for the WMT 20 News Translation Task (2020)

Abstract: This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks.

## introduction the adapt centre participated in the news translation shared task of the fifth conference of machine translation (wmt20) in the english-to-tamil and tamil-to-english language directions. to build our neural mt systems we used the transformer model @xcite . our strategies to build the competitive mt systems for the task include applying the state-of-the-art data augmentation approaches (e.g. @xcite ), selecting "pseudo" indomain monolingual sentences for the creation of synthetic bitexts, mining monolingual source and target sentences for the adaptation of neural mt (nmt) systems, finding the optimal set of hyperparameters for transformer as far as low-resource translation is concerned. the remainder of the paper is organized as follows. in section 2, we present our approaches for the mt system building. section 3 first presents details of the data sets used and then presents the evaluation results with some discussions, while section 4 concludes our work with avenues for future work. ## conclusion this paper presents the adapt system description for the wmt20 news translation shared task. we participated in the english-to-tamil and tamil-to-english tasks. english-tamil is a low-resource language-pair and we used the data provided by the wmt20 organisers only. given the limited resources provided for the tasks, we aimed to build the competitive translation systems for the competition. for this, we applied a variety of strategies, e.g. iterative data augmentation, selection of pseudo in-domain sentences, and a novel strategy for the adaptation of the nmt models to the task. we found that the systematic addition of these techniques to baseline yields excellent improvements over the baseline. this paper presented an effective adaptation method for the nmt systems. this method is found to be effective as far as the translation task we participated in is concerned. in the future, we aim to test on-the-fly adaptation method @xcite to translate domain texts.