WebNLG Challenge 2020: Language Agnostic Delexicalisation for Multilingual RDF-to-text generation

Giulio Zhou, Gerasimos Lampouras


Abstract
This paper presents our submission to the WebNLG Challenge 2020 for the English and Russian RDF-to-text generation tasks. Our first of three submissions is based on Language Agnostic Delexicalisation, a novel delexicalisation method that match values in the input to their occurrences in the corresponding text through comparison of pretrained multilingual embeddings, and employs a character-level post-editing model to inflect words in their correct form during relexicalisation. Our second submission forfeits delexicalisation and uses SentencePiece subwords as basic units. Our third submission combines the previous two by alternating between the output of the delexicalisation-based system when the input contains unseen entities and/or properties and the output of the SentencePiece-based system when the input is seen during training.
Anthology ID:
2020.webnlg-1.22
Volume:
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)
Month:
12
Year:
2020
Address:
Dublin, Ireland (Virtual)
Editors:
Thiago Castro Ferreira, Claire Gardent, Nikolai Ilinykh, Chris van der Lee, Simon Mille, Diego Moussallem, Anastasia Shimorina
Venue:
WebNLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
186–191
Language:
URL:
https://aclanthology.org/2020.webnlg-1.22
DOI:
Bibkey:
Cite (ACL):
Giulio Zhou and Gerasimos Lampouras. 2020. WebNLG Challenge 2020: Language Agnostic Delexicalisation for Multilingual RDF-to-text generation. In Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+), pages 186–191, Dublin, Ireland (Virtual). Association for Computational Linguistics.
Cite (Informal):
WebNLG Challenge 2020: Language Agnostic Delexicalisation for Multilingual RDF-to-text generation (Zhou & Lampouras, WebNLG 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/2020.webnlg-1.22.pdf