Vernacular Search Query Translation with Unsupervised Domain Adaptation
This addresses the challenge of enabling cross-lingual information retrieval for diverse users on e-commerce platforms, though it is incremental as it builds on existing translation models with domain adaptation.
The paper tackles the problem of translating vernacular search queries for e-commerce without parallel training data by proposing an unsupervised domain adaptation approach, achieving over 20 BLEU points improvement over a baseline without parallel corpus and over 27 BLEU points with a small labeled set.
With the democratization of e-commerce platforms, an increasingly diversified user base is opting to shop online. To provide a comfortable and reliable shopping experience, it's important to enable users to interact with the platform in the language of their choice. An accurate query translation is essential for Cross-Lingual Information Retrieval (CLIR) with vernacular queries. Due to internet-scale operations, e-commerce platforms get millions of search queries every day. However, creating a parallel training set to train an in-domain translation model is cumbersome. This paper proposes an unsupervised domain adaptation approach to translate search queries without using any parallel corpus. We use an open-domain translation model (trained on public corpus) and adapt it to the query data using only the monolingual queries from two languages. In addition, fine-tuning with a small labeled set further improves the result. For demonstration, we show results for Hindi to English query translation and use mBART-large-50 model as the baseline to improve upon. Experimental results show that, without using any parallel corpus, we obtain more than 20 BLEU points improvement over the baseline while fine-tuning with a small 50k labeled set provides more than 27 BLEU points improvement over the baseline.