Improving Norwegian Translation of Bicycle Terminology Using Custom Named-Entity Recognition and Neural Machine Translation

Abstract

The Norwegian business-to-business (B2B) market for bicycles consists mainly of international brands, such as Shimano, Trek, Cannondale, and Specialized. The product descriptions for these brands are usually in English and need local translation. However, these product descriptions include bicycle-specific terminologies that are challenging for online translators, such as Google. For this reason, local companies outsource translation or translate product descriptions manually, which is cumbersome. In light of the Norwegian B2B bicycle industry, this paper explores transfer learning to improve the machine translation of bicycle-specific terminology from English to Norwegian, including generic text. Firstly, we trained a custom Named-Entity Recognition (NER) model to identify cycling-specific terminology and then adapted a MarianMT neural machine translation model for the translation process. Due to the lack of publicly available bicycle-terminology-related datasets to train the proposed models, we created our dataset by collecting a corpus of cycling-related texts. We evaluated the performance of our proposed model and compared its performance with that of Google Translate. Our model outperformed Google Translate on the test set, with a SacreBleu score of 45.099 against 36.615 for Google Translate on average. We also created a web application where the user can input English text with related bicycle terminologies, and it will return the detected cycling-specific words in addition to a Norwegian translation.