Soumya Chennabasavraj


Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation
Kamal Kumar Gupta | Soumya Chennabasavraj | Nikesh Garera | Asif Ekbal
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

Availability of the user reviews in vernacular languages is helpful for the users to get information regarding the products. Since most of the e-commerce websites allow the reviews in English language only, it is important to provide the translated versions of the reviews to the non-English speaking users. Translation of the user reviews from English to vernacular languages is a challenging task, predominantly due to the lack of sufficient in-domain datasets. In this paper, we present a pre-training based efficient technique which is used to adapt and improve the single multilingual neural machine translation (NMT) model for the low-resource language pairs. The pre-trained model contains a special synthetic cross-lingual decoder. The decoder for the pre-training is trained over the cross-lingual target samples where the phrases are replaced with their translated counterparts. After pre-training, the model is adapted to multiple samples of the low-resource language pairs using incremental learning that does not require full training from the very scratch. We perform the experiments over eight low-resource and three high resource language pairs from the generic domain, and two language pairs from the product review domains. Through our synthetic multilingual decoder based pre-training, we achieve improvements of upto 4.35 BLEU points compared to the baseline and 2.13 BLEU points compared to the previous code-switched pre-trained models. The review domain outputs from the proposed model are evaluated in real time by human evaluators in the e-commerce company Flipkart.