Meaning-Game-based Centering Model with Statistical Definition of Utility of Referential Expression and Its Verification Using Japanese and English Corpora

This paper presents a quantitative modeling of referential coherence by which conversation systems measure the smoothness of discourse. Investigations of the corpora show that referential coherence depends on languages or genres of discourse. Our goal is to establish a quantitative model that can be statistically adapted to various languages. Centering theory explains referential coherence by using heuristic rules. Since these heuristics should be invented manually for a particular language, we need a quantitative/statistical model that can be obtained from a corpus. The meaning-game-based centering model (MGCM) (Shiramatsu et al., 2005) quantitatively reformulates centering theory by exploiting quantitative aspects of game theory. It quantifies referential coherence by using the two parameters related to salience and pronominalization: “reference probability” and “perceptual utility”. However, MGCM still has two problems. The first is that perceptual utility cannot be statistically adapted to various languages. The second is that MGCM has only been verified in Japanese. We have enhanced the model by statistically defining perceptual utility. Specifically, we defined it by using occurrence frequency of the referential expression in a corpus. Experimental results using English and Japanese corpora showed that our statistical definitions enabled the parameters to be adapted to both corpora. Furthermore, the statistical tests of the enhanced MGCM showed its validity in both corpora.