Multi-lingual neural title generation for e-Commerce browse pages

TL;DR

This paper presents a multi-lingual sequence-to-sequence model leveraging transfer learning to generate e-Commerce browse page titles across multiple languages, including low-resource languages like French.

Contribution

It introduces a joint multi-lingual model for title generation that performs well on both high- and low-resource languages, reducing reliance on large language-specific datasets.

Findings

Effective title generation in English, German, and French.

Improved performance on low-resource French language.

Demonstrates transfer learning benefits in multi-lingual settings.

Abstract

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.