Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse

Fahime Same, Kees van Deemter


Abstract
First, we discuss the most common linguistic perspectives on the concept of recency and propose a taxonomy of recency metrics employed in Machine Learning studies for choosing the form of referring expressions in discourse context. We then report on a Multi-Layer Perceptron study and a Sequential Forward Search experiment, followed by Bayes Factor analysis of the outcomes. The results suggest that recency metrics counting paragraphs and sentences contribute to referential choice prediction more than other recency-related metrics. Based on the results of our analysis, we argue that, sensitivity to discourse structure is important for recency metrics used in determining referring expression forms.
Anthology ID:
2020.codi-1.12
Volume:
Proceedings of the First Workshop on Computational Approaches to Discourse
Month:
November
Year:
2020
Address:
Online
Venue:
CODI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–123
Language:
URL:
https://aclanthology.org/2020.codi-1.12
DOI:
10.18653/v1/2020.codi-1.12
Bibkey:
Cite (ACL):
Fahime Same and Kees van Deemter. 2020. Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse. In Proceedings of the First Workshop on Computational Approaches to Discourse, pages 113–123, Online. Association for Computational Linguistics.
Cite (Informal):
Computational Interpretations of Recency for the Choice of Referring Expressions in Discourse (Same & van Deemter, CODI 2020)
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PDF:
https://preview.aclanthology.org/author-url/2020.codi-1.12.pdf
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