CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Multilingual email zoning
Authors
Mariana S.C. Almeida
Bruno Jardim
Ricardo Rei
Publication date
1 January 2021
Publisher
'Association for Computational Linguistics (ACL)'
View
on
arXiv
Abstract
Jardim, B., Rei, R., & Almeida, M. S. C. (2021). Multilingual email zoning. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Student Research Workshop (pp. 88-95). (EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Student Research Workshop). Association for Computational Linguistics (ACL). --------------------------- Funding Information: This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 873904. Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 873904. Publisher Copyright: © 2021 Association for Computational LinguisticsThe segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails. However, despite the multilingual character of emails and their applications, previous literature regarding email zoning corpora and systems was developed essentially for English. In this paper, we analyse the existing email zoning corpora and propose a new multilingual benchmark composed of 625 emails in Portuguese, Spanish and French. Moreover, we introduce OKAPI, the first multilingual email segmentation model based on a language agnostic sentence encoder. Besides generalizing well for unseen languages, our model is competitive with current English benchmarks, and reached new state-of-the-art performances for domain adaptation tasks in English.publishersversionpublishe
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Repositório da Universidade Nova de Lisboa
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:run.unl.pt:10362/135883
Last time updated on 20/05/2022