Сюрпризал

This paper introduces a psycholinguistic model of sentence processing which combines a Hidden Markov Model noun phrase chun- ker with a co-reference classifier. Both mod- els are fully incremental and …...

AbstractWhile previous research on readability has typically focused on document-level measures, recent work in areas such as natural language generation has pointed out the need of sentence-level readability measures. Much …...

AbstractTransformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly. In this …...

AbstractThe uniform information density (UID) hypothesis posits a preference among language users for utterances structured such that information is distributed uniformly across a signal. While its implications on language production …...

AbstractWe focus on the syntactic variation and measure syntactic distances between nine Slavic languages (Belarusian, Bulgarian, Croatian, Czech, Polish, Slovak, Slovene, Russian, and Ukrainian) using symmetric measures of insertion, deletion …...

AbstractIn psycholinguistics, semantic attraction is a sentence processing phenomenon in which a given argument violates the selectional requirements of a verb, but this violation is not perceived by comprehenders due …...

AbstractWe explore the relationship between information density/surprisal of source and target texts in translation and interpreting in the language pair English-German, looking at the specific properties of translation (“translationese”). Our …...

AbstractThis work presents a linguistic analysis into why larger Transformer-based pre-trained language models with more parameters and lower perplexity nonetheless yield surprisal estimates that are less predictive of human reading …...