Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing this question. Evaluating on two large readability corpora, we find that, given sufficient training data, augmenting deep learning models with linguistically motivated features does not improve state-of-the-art performance. Our results provide preliminary evidence for the hypothesis that the state-of-theart deep learning models represent linguistic features of the text related to readability. Future research on the nature of representations formed in these models can shed light on the learned features and their relations to linguistically motivated ones hypothesized in traditional approaches.

The past 15 years have seen increasing experimental investigations of core pragmatic questions in the ever more active and lively field of experimental pragmatics. Within experimental pragmatics, many of the core questions have relied on the operationalization of the theoretical notion of “implicature rate.” Implicature rate based results have informed the work on acquisition, online processing, and scalar diversity, inter alia. Implicature rate has typically been quantified as the proportion of “pragmatic” judgments in two-alternative forced choice truth value judgment tasks. Despite its theoretical importance, this linking hypothesis from implicature rate to behavioral responses has never been extensively tested. Here we show that two factors dramatically affect the “implicature rate” inferred from truth value judgment tasks: (a) the number of responses provided to participants; and (b) the linking hypothesis about what constitutes a “pragmatic” judgment. We argue that it is time for the field of experimental pragmatics to engage more seriously with its foundational assumptions about how theoretical notions map onto behaviorally measurable quantities, and present a sketch of an alternative linking hypothesis that derives behavior in truth value judgment tasks from probabilistic utterance expectations.

What are the constraints, cues, and mechanisms that help learners create successful word-meaning mappings? This study takes up linguistic disjunction and looks at cues and mechanisms that can help children learn the meaning of or. We first used a large corpus of parent-child interactions to collect statistics on or uses. Children started producing or between 18-30 months and by 42 months, their rate of production reached a plateau. Second, we annotated for the interpretation of disjunction in child-directed speech. Parents used or mostly as exclusive disjunction, typically accompanied by rise-fall intonation and logically inconsistent disjuncts. But when these two cues were absent, disjunction was generally not exclusive. Our computational modeling suggests that an ideal learner could successfully interpret an English disjunction (as exclusive or not) by mapping forms to meanings after partitioning the input according to the intonational and logical cues available in child-directed speech.

The origins of logical concepts is one of the central topics in cognitive science. Cesana-Arlotti and colleagues provide novel eye-tracking measures from preverbal infants compatible withdisjunctive reasoning. However, the evidence is not conclusive. We provide a simpler object tracking account that would produce the same processing signatures. Future research must make a priori predictions that distinguish these accounts.

The Persian object marker rā is called many things, among them: marker of specificity (Karimi 1990), definiteness (Mahootian 1997), secondary topics (Dabir-Moghaddam 1992), and presuppositions (Ghomeshi 1996). These accounts capture the core of what rā is, yet also include a lot of what rā is not. I report novel examples that show rā is not an (exclusive) marker of specific or definite referents. It is also not an (exclusive) marker of (secondary) topics. Instead, rā’s core contribution is something shared by all these accounts: old or presupposed information. I show that the information presupposed by rā is an existence implication. A marked object like sandali-ro (“chair”-rā) implies that there is one or more chairs in the conversational context. This account captures several novel observations on the distribution of rā such as its optional presence on proper names in some contexts. I provide a formal and compositional analysis of simple Persian sentences with definite and indefinite objects.