A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context

Proceedings of the 28th International Conference on Computational Linguistics, pages 4575–4586 Barcelona, Spain (Online), December 8-13, 2020 4575 A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context Fahime Same University of Cologne f.same@uni-koeln.de Kees van Deemter Utrecht University c.j.vandeemter@uu.nl Abstract This paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context. Based on this evaluation, we selected seven feature sets from the literature, amounting to 65 distinct linguistic features. The features were then grouped into 9 broad classes. After building Random Forest models, we used Feature Importance Ranking and Sequential Forward Search methods to assess the “importance” of the features. Combining the results of the two methods, we propose a consensus feature set. The 6 features in our consensus set come from 4 different classes, namely grammatical role, inherent features of the referent, antecedent form and recency. 1 Introduction Various studies have raised the question of which factors play a role in the choice of referring expressions. One of the main ideas in this tradition (henceforth, the linguistic tradition) is that there is a direct rela- tionship between the “prominence” (in a broad sense) of a referent at a given point in the discourse, and the form used to refer to it. If a referent is prominent, a short anaphoric form (e.g. a pronoun) suffices; if it is less prominent, longer forms with more semantic content are used. Prominence has been argued to be influenced by various factors such as recency and frequency of mention (Ariel, 1990), grammatical function (Brennan, 1995), distance (McCoy and Strube, 1999), animacy (Fukumura and van Gompel, 2011), and competition between the referents (Arnold and Griffin, 2007). Reference production is also one of the most-studied topics in Natural Language Generation (Gatt and Krahmer, 2018), where it is known as Referring Expression Generation [REG] (Krahmer and van Deemter, 2019). A key part of the REG problem is deciding which form (e.g., proper name, definite description or pronoun) to employ to refer to a referent at a given point in the discourse. Henceforth, we call this task Selection of Referential Form (SRF). SRF models come in many shapes and forms, with feature-based Machine Learning (ML) models playing a dominant role. However, the feature sets employed by these models can differ considerably from one model to the next, and although features akin to the ones employed in the linguistic tradition are often used, other types of features, which are harder to interpret linguistically, are frequent as well. Our aim in this paper is to examine feature-based SRF models from a linguistic perspective. We will conduct a systematic evaluation of these models, asking what features make them work best. Having done this, we propose a “consensus” feature set. Finally, we compare the features in our consensus feature set against the factors considered to be important in the linguistic tradition. An important question in any systematic evaluation is how the objects of study …

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