The ADAPT Centre’s Participation in WAT 2020 E nglish-to- O dia Translation Task (2020)

Abstract: This paper describes the ADAPT Centre sub-missions to WAT 2020 for the English-to-Odia translation task. We present the approaches that we followed to try to build competitive machine translation (MT) systems for English-to-Odia. Our approaches include monolingual data selection for creating synthetic data and identifying optimal sets of hyperparameters for the Transformer in a low-resource scenario. Our best MT system produces 4.96BLEU points on the evaluation test set in the English-to-Odia translation task.

## introduction the adapt centre participated in the english-to-odia shared task at the 7th workshop on asian translation (wat 2020) @xcite . 1 this paper presents the approaches we adopted in order to try to build competitive mt systems for this translation task. we also discuss methods that did not work for us. our nmt systems are state-ofthe-art transformer models @xcite . this paper is organized as follows. section 2 presents our approaches. we describe the resources we utilized for training in section 3. section 4 presents the results obtained, and section 5 concludes our work with avenues for future work. ## data used we made use of both the parallel and monolingual data that were provided by the wat 2020 task organisers. 2 additionally, we used external monolingual data for system building. the statistics of the parallel and monolingual corpora (opusnlp, foot_2 oscar foot_3 and ai4bharat-indicnlp) foot_4 are shown in tables 1 and 2 , respectively. in order to remove noisy sentences from the corpus, we used a language identifier cld2 foot_5 with a confidence of 95. ## results and discussion we used the state-of-the-art transformer model in order to prepare our mt systems. for system building, we used the opennmt toolkit @xcite . in order to evaluate our mt systems, we used the widely-used evaluation metric, bleu @xcite . ## conclusions this paper presents the adapt centre system description for the wat 2020 english-to-odia translation shared task. our best mt model, a transformer model prepared using an optimal set of hyperparameters, obtain 4.96 bleu points on the evaluation test set. we selected those monolingual sentences from a large monolingual data that are similar in terms of style and domain to the sentences of the parallel corpus. we then created a synthetic parallel corpus by translating the selected odia monolingual sentences to english. we finetuned our baseline mt system on the training data that combines of the synthetic and original parallel corpora. this strategy did not work for us since using synthetic data causes to deteriorate the performance of the english-to-odia mt system. as for future work, we aim to explore transfer learning and using data of other related languages in order to improve translation of the english-to-odia mt system.