Encoding information structure in Yucatec Maya : on the Interplay of prosody and syntax

The aim of this paper is to outline the means for encoding information structure in Yucatec Maya. Yucatec Maya is a tone language, displaying a three-fold opposition in the tonal realization of syllables. From the morpho-syntactic point of view, the grammar of Yucatec Maya contains morphological (topic affixes, morphological marking of out-of-focus predicates) and syntactic (designated positions) means to uniquely specify syntactic constructions for their information structure. After a descriptive overview of these phenomena, we present experimental evidence which reveals the impact of the nonavailability of prosodic alternatives on the choice of syntactic constructions in language production

An exemplar model should be able to explain all syntactic priming phenomena : a commentary on Ambridge (2020)

The authors argue that Ambridge’s radical exemplar account of language cannot clearly explain all syntactic priming evidence, such as inverse preference effects (greater priming for less frequent structures), and the contrast between short-lived lexical boost and long-lived abstract priming. Moreover, without recourse to a level of abstract syntactic structure, Ambridge’s account cannot explain abstract priming in amnesia patients or cross-linguistic priming. Instead, the authors argue that abstract representations remain the more parsimonious account for the wide variety of syntactic priming phenomena

Syntactic Doubling and the Structure of Chains

A recent survey of 267 dialects of Dutch (SAND; Barbiers et al. 2005) provides 6 cases of doubling in syntactic chains

Syntactic and semantic contributions of pitch accents during sentence comprehension

Syntactic, semantic, and prosodic cues all establish expectations that guide sentence comprehension. In the prosodic domain, pitch accents can assign contrastive focus and resolve a syntactically ambiguous phrase. However, can prosodic focus marking (by pitch accenting) influence the interpretation of a sentence in the presence of syntactic and semantic cues? Our auditory experiment revolved around the sentence (in German) “Yesterday the policeman arrested the thief, not the murderer”. A pitch accent on either POLICEMAN or THIEF placed one of those arguments in contrastive focus with the ellipsis structure ("the murderer”). The two contrasted arguments could contain violations: in the syntax condition, the grammatical case of the article in the ellipsis structure mismatched the focused constituent in the main clause (nominative vs. accusative). In the semantic condition, the thematic roles of the contrasted words were incongruent (typical agent vs. patient roles of “arrest”). Visual comprehension questions probed the agent/patient role of the arguments in the sentence (subject or object), followed by a button-press response. Reaction times showed that if the pitch accent marked syntactic information that mismatched the syntactic information in the ellipsis structure, responses were delayed. The direction of the semantic effect depended on the focused noun. The response patterns showed that participants were led by the syntactic information to make their syntactic judgements, despite a conflicting expectation established by prosody. The experiment shows that pitch accents establish a syntactic expectation during sentence comprehension. However, these expectations are overwritten by incoming syntactic information to yield an interpretation of the sentence

Combining semantic and syntactic structure for language modeling

Structured language models for speech recognition have been shown to remedy the weaknesses of n-gram models. All current structured language models are, however, limited in that they do not take into account dependencies between non-headwords. We show that non-headword dependencies contribute to significantly improved word error rate, and that a data-oriented parsing model trained on semantically and syntactically annotated data can exploit these dependencies. This paper also contains the first DOP model trained by means of a maximum likelihood reestimation procedure, which solves some of the theoretical shortcomings of previous DOP models.Comment: 4 page