Multi-lingual neural title generation for e-Commerce browse pages
This addresses the need for scalable title generation in e-commerce to improve searchability, though it is incremental as it builds on existing neural methods with a multi-lingual focus.
The paper tackles the problem of automatically generating titles for e-commerce browse pages across multiple languages, using sequence-to-sequence models with transfer learning to handle both high- and low-resourced languages, achieving performance evaluated on English, German, and French.
To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of easily searchable browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high- & low-resourced languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on low-resourced French language.