A Baseline Document Planning Method for Automated Journalism

no code implementations • NoDaLiDa 2021 • Leo Leppänen, Hannu Toivonen

In this work, we present a method for content selection and document planning for automated news and report generation from structured statistical data such as that offered by the European Union’s statistical agency, EuroStat.

Using contextual and cross-lingual word embeddings to improve variety in template-based NLG for automated journalism

no code implementations • EACL (Hackashop) 2021 • Miia Rämö, Leo Leppänen

In this work, we describe our efforts in improving the variety of language generated from a rule-based NLG system for automated journalism.

Cross-Lingual Word Embeddings Sentence +1

EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions

no code implementations • EACL (Hackashop) 2021 • Senja Pollak, Marko Robnik-Šikonja, Matthew Purver, Michele Boggia, Ravi Shekhar, Marko Pranjić, Salla Salmela, Ivar Krustok, Tarmo Paju, Carl-Gustav Linden, Leo Leppänen, Elaine Zosa, Matej Ulčar, Linda Freienthal, Silver Traat, Luis Adrián Cabrera-Diego, Matej Martinc, Nada Lavrač, Blaž Škrlj, Martin Žnidaršič, Andraž Pelicon, Boshko Koloski, Vid Podpečan, Janez Kranjc, Shane Sheehan, Emanuela Boros, Jose G. Moreno, Antoine Doucet, Hannu Toivonen

This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.

Underreporting of errors in NLG output, and what to do about it

no code implementations • INLG (ACL) 2021 • Emiel van Miltenburg, Miruna-Adriana Clinciu, Ondřej Dušek, Dimitra Gkatzia, Stephanie Inglis, Leo Leppänen, Saad Mahamood, Emma Manning, Stephanie Schoch, Craig Thomson, Luou Wen

We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make.

Position Text Generation