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nà-cleft (non-)exhaustivity
(2021)
This paper presents two experimental studies on the exhaustive inference associated with focus-background na-clefts in Akan (among others, Boadi 1974; Duah 2015; Grubic & Renans & Duah 2019; Titov 2019), with a direct comparison to two recent experiments on German es-clefts employing an identical design (De Veaugh-Geiss et al. 2018). Despite the unforeseen response patterns in Akan in the incremental information-retrieval paradigm used, a post-hoc exploratory analysis reveals compelling parallels between the two languages. The results are compatible with a unified approach both (i) cross-linguistically between Akan and German; and (ii) cross-sententially between na-clefts (a na P, 'It is a who did P') and definite pseudoclefts, i.e., definite descriptions with identity statements (Nipa no a P ne a, 'The person who did P is a') (Boadi 1974; Ofori 2011). Participant variability in (non-)exhaustive interpretations is compatible with discourse pragmatic approaches to cleft exhaustivity (Pollard & Yasavul 2016; De Veaugh-Geiss et al. 2018; Titov 2019).
In successful communication, the literal meaning of linguistic utterances is often enriched by pragmatic inferences. Part of the pragmatic reasoning underlying such inferences has been successfully modeled as Bayesian goal recognition in the Rational Speech Act (RSA) framework. In this paper, we try to model the interpretation of question-answer sequences with narrow focus in the answer in the RSA framework, thereby exploring the effects of domain size and prior probabilities on interpretation. Should narrow focus exhaustivity inferences be actually based on Bayesian inference involving prior probabilities of states, RSA models should predict a dependency of exhaustivity on these factors. We present experimental data that suggest that interlocutors do not act according to the predictions of the RSA model and that exhaustivity is in fact approximately constant across different domain sizes and priors. The results constitute a conceptual challenge for Bayesian accounts of the underlying pragmatic inferences.