Improving Machine Translation in the E -commerce Luxury Space. A case study (2023)

Abstract: This case study presents a Multilingual e-commerce Project, which principal aim is to create an improved system that translates product titles and descriptions, plus other content in multiple languages. The project consisted of two main phases; a research-intensive solution using state-of-the-art Machine Translation systems and baseline language models for two language pairs, and the development of a Machine Translation system. The features implemented included Quality Estimation, model benchmarking, entity recognizers, and automatic domain detection. mBART model was used to create the system for the specific domain of e-commerce, for luxury items.

## introduction machine translation (mt) is the automatic translation of text from one language to another without human intervention @xcite . when this translation is performed using deep neural networks (dnn), it is referred to as neural machine translation (nmt) @xcite . nmt technology has made significant progress in recent years, however, they have generally been trained on domain-general data, directly affecting domainspecific translations @xcite . according to @xcite , most methods for adapting mt systems to a specific domain focus on fine-tuning. one of those specific domains is e-commerce. in today's globalized world, e-commerce has be-come a crucial part of the economy. with the rise of online shopping, businesses must be able to communicate with customers in their native languages to provide a seamless shopping experience. however, translating product titles and descriptions, reviews, and other content while maintaining formal and informal styles, and dealing with lengthy and very short sentences, can be a daunting task. in this work, an e-commerce multilingual project aimed to improve machine translation quality is introduced. this improvement was carried out with mbart model @xcite , which is leading the way in multilingual translation. this model is designed to handle multiple languages simultaneously, making it ideal for ecommerce applications where content needs to be translated quickly and accurately. the project has been led by acclaro foot_0 , a leading company with extensive experience in professional translation services. ## arquitectures in the state-of-the-art (sota) of nmt, the architectures proposed by @xcite al. (2019), liu et al. (2020) and tang et al. (2020) stand out. on the one hand, gpt-2 model proposed by @xcite is based on the architecture of large transformers @xcite . besides, gpt-2 follows the details of the openai gpt model proposed by @xcite . on the other hand, according to @xcite , mbart is "a multilingual sequence-to-sequence denoising auto-encoder" that uses bart @xcite large-scale monolingual corpora across many languages. this model was pre-trained using a subset of data in 25 languages extracted from common crawl foot_1 . finally, @xcite add to mbart the ability to perform multilingual finetuning and extend it to 50 languages without training from scratch. this paper refers to these mbart-based models as mbart25 and mbart50, respectively. the mbart-based models were selected for their ability to surpass the state-of-the-art results in the english-german and english-french language pairs. the evaluation was performed using the bleu measure @xcite , which compared the output of machine translation systems with human reference translations. these results are in agreement with those reported by @xcite . it should be noted that these architectures are implemented in nmt framework fairseq foot_2 @xcite . finally, other state-of-the-art architectures were taken into account, including m2m-100 @xcite , @xcite , opennmt @xcite and @xcite . however, it should be noted that these architectures were only analyzed and not implemented. ## methodology figure 1 represents the methodology applied in the luxury e-commerce multilingual project. initially, a baseline was defined to set the minimum acceptable performance (see section 3.2). then, the sentence pairs to be processed and filtered within the e-commerce domain were established (see section 3.3). the best quality pairs were used to train and finetune the models (see section 3.4). the trained models obtained were evaluated and compared with the initial baseline or the baseline of the previous iteration (see section 3.5). if the model performance improves the baseline, it is deployed using rest api services (see section 3.6) and a new baseline is established. finally, the errors detected in the translations of these models were sent to expert linguists for examination, therefore improving the training pairs for the next iteration. ## results the tables 1 and 2 show the values of the bleu measure obtained on pairs of product titles and descriptions. these results are shown concerning to the quarters of the year 2022. the first three quarters were evaluated with mbart25 while the last one with mbart50. the initial baseline was the bleu obtained by deepl.