Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities

Rossana Cunha, Thiago Castro Ferreira, Adriana Pagano, Fabio Alves


Abstract
Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds. A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process. To solve this problem, we propose two extensions to NeuralREG, a state-of-the-art encoder-decoder REG model. The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model. Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work. Code and all produced data are publicly available.
Anthology ID:
2020.coling-main.205
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2261–2272
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.205
DOI:
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http://aclanthology.lst.uni-saarland.de/2020.coling-main.205.pdf