Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction

Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction Ashutosh Modi1,3 Ivan Titov2,4 Vera Demberg1,3 Asad Sayeed1,3 Manfred Pinkal1,3 1 {ashutosh,vera,asayeed,pinkal}@coli.uni-saarland.de 2 titov@uva.nl 3 Universit¨at des Saarlandes, Germany 4 ILLC, University of Amsterdam, the Netherlands Abstract Recent research in psycholinguistics has pro- vided increasing evidence that humans predict upcoming content. Prediction also affects per- ception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. lin- guistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences refer- ring expression type but do not find evidence for such an effect. 1 Introduction Being able to anticipate upcoming content is a core property of human language processing (Kutas et al., 2011; Kuperberg and Jaeger, 2016) that has re- ceived a lot of attention in the psycholinguistic liter- ature in recent years. Expectations about upcoming words help humans comprehend language in noisy settings and deal with ungrammatical input. In this paper, we use a computational model to address the question of how different layers of knowledge (lin- guistic knowledge as well as common-sense knowl- edge) influence human anticipation. Here we focus our attention on semantic pre- dictions of discourse referents for upcoming noun phrases. This task is particularly interesting because it allows us to separate the semantic task of antic- ipating an intended referent and the processing of the actual surface form. For example, in the con- text of I ordered a medium sirloin steak with fries. Later, the waiter brought ..., there is a strong ex- pectation of a specific discourse referent, i.e., the referent introduced by the object NP of the preced- ing sentence, while the possible referring expression could be either the steak I had ordered, the steak, our food, or it. Existing models of human predic- tion are usually formulated using the information- theoretic concept of surprisal. In recent work, how- ever, surprisal is usually not computed for DRs, which represent the relevant semantic unit, but for the surface form of the referring expressions, even though there is an increasing amount of literature suggesting that human expectations at different lev- els of representation have separable effects on pre- diction and, as a consequence, that the modelling of only one level (the linguistic surface form) is in- sufficient (Kuperberg and Jaeger, 2016; Kuperberg, 2016; Zarcone et al., 2016). The present model ad- dresses this shortcoming by explicitly modelling and representing common-sense knowledge and concep- tually separating the semantic (discourse referent) and the surface level (referring expression) expec- tations. Our discourse referent prediction task is related to the NLP task of coreference resolution, but it substantially differs from that task in the following ways: 1) we use only the incrementally available left context, while coreference resolution …

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