Corpus, experimental and modeling investigations of cross-linguistic differences in pronoun resolution preferences. Issue 1 (14th May 2021)

• Record Type: Journal Article
• Authors: Schulz, Miriam; Burnett, Heather; Hemforth, Barbara
• Is Part Of: Glossa. Volume 6:Issue 1(2021)
• Publication Date: 2021-05-14
• Subjects: alternative constructions -- pronoun resolution -- corpus study -- Rational Speech Act model; Linguistics -- Periodicals; Language and languages -- Periodicals; 410.5
• DOI: 10.5334/gjgl.1142
• Languages: English
• ISSNs: 2397-1835

Abstract

Abstract : We investigate the impact of syntactic alternatives on pronoun resolution in ambiguous constructions in English and French. Previous research detected language-specific preferences in pronoun resolution in utterances of the type "The postman met the streetsweeper before he went home". These preferences have been attributed to the interaction of information structural and syntactic constraints inducing a subject bias on the one hand, and Gricean reasoning processes taking into account alternative syntactic constructions on the other hand. A corpus study of four English and French corpora shows that an alternative construction which takes a subject antecedent ("The postman met the streetsweeper before going home") is much less frequent in spoken English than French. A Rational Speech Act (RSA) model with corpus frequencies integrated as language-specific costs on the use of each construction makes empirical predictions for pronoun resolution preferences in French and English for sentences with "avant"/"before" which have been tested before but also for sentences with "après"/"after" which have not been tested so far. New experimental data show a very good fit of the model predictions for pronoun resolution preferences in English as well as for the differences in antecedent choices between French and English. However, experimental data showing differences in antecedent choices between French sentences with "après" and "avant" deviate from model predictions, indicating that more factors need to be taken into account. The combination of Bayesian modeling, corpus analyses and experimental data shows that RSA models can make relevant and falsifiable predictions for cross-linguistic variation in processing.