Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts

TL;DR

This paper presents a large, diverse corpus of anaphoric reference annotations for fiction and Wikipedia texts, created using a game-based approach combined with a new resolve-and-aggregate method to improve annotation efficiency and coverage.

Contribution

Introducing a new large-scale anaphoric reference corpus for diverse genres, utilizing a novel resolve-and-aggregate paradigm with game-based annotation to overcome previous dataset limitations.

Findings

The corpus is comparable in size to the largest existing datasets.

The method accelerates annotation and improves coverage of genres and document lengths.

It includes annotations for singletons and non-referring expressions.

Abstract

Although several datasets annotated for anaphoric reference/coreference exist, even the largest such datasets have limitations in terms of size, range of domains, coverage of anaphoric phenomena, and size of documents included. Yet, the approaches proposed to scale up anaphoric annotation haven't so far resulted in datasets overcoming these limitations. In this paper, we introduce a new release of a corpus for anaphoric reference labelled via a game-with-a-purpose. This new release is comparable in size to the largest existing corpora for anaphoric reference due in part to substantial activity by the players, in part thanks to the use of a new resolve-and-aggregate paradigm to 'complete' markable annotations through the combination of an anaphoric resolver and an aggregation method for anaphoric reference. The proposed method could be adopted to greatly speed up annotation time in other projects involving games-with-a-purpose. In addition, the corpus covers genres for which no comparable size datasets exist (Fiction and Wikipedia); it covers singletons and non-referring expressions; and it includes a substantial number of long documents (> 2K in length).