Multilingual Email Zoning - Segmenting Multilingual Email Text Into Zones

Abstract

The segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails. In this research, we analyze in depth the email zoning literature and develop a business case around CLEVERLY AI, a company from the Customer Service sector. We design a new email zoning classification schema and collect a multilingual corpus of emails from CLEVERLY AI clients. We develop five neural network-based email zoning systems, among those systems, we introduce OKAPI, the first multilingual email zoning model based on a language agnostic sentence encoder. Besides outperforming our other systems when tested on CLEVERLY’s emails, OKAPI shows competitive performances with current English public benchmarks and reached new state-of-the-art results for English domain adaptation tasks. Moreover, we release a new multilingual benchmark, composed of 625 emails in Portuguese, Spanish and French, and demonstrate OKAPI can effectively generalize its learnings for unseen languages.