The role of working memory and contextual constraints in children's processing of relative clauses

An auditory sentence comprehension task investigated the extent to which the integration of contextual and structural cues was mediated by verbal memory span with 32 English-speaking 6- to 8-year old children. Spoken relative clause sentences were accompanied by visual context pictures which fully (depicting the actions described within the relative clause) or partially (depicting several referents) met the pragmatic assumptions of relativisation. Comprehension of the main and relative clauses of centre-embedded and right-branching structures was compared for each context. Pragmatically-appropriate contexts exerted a positive effect on relative clause comprehension, but children with higher memory spans demonstrated a further benefit for main clauses. Comprehension for centre-embedded main clauses was found to be very poor, independently of either context or memory span. The results suggest that children have access to adult-like linguistic processing mechanisms, and that sensitivity to extra-linguistic cues is evident in young children and develops as cognitive capacity increases

Semantic indeterminacy in object relative clauses

This article examined whether semantic indeterminacy plays a role in comprehension of complex structures such as object relative clauses. Study 1 used a gated sentence completion task to assess which alternative interpretations are dominant as the relative clause unfolds; Study 2 compared reading times in object relative clauses containing different animacy configurations to unambiguous passive controls; and Study 3 related completion data and reading data. The results showed that comprehension difficulty was modulated by animacy configuration and voice (active vs. passive). These differences were well correlated with the availability of alternative interpretations as the relative clause unfolds, as revealed by the completion data. In contrast to approaches arguing that comprehension difficulty stems from syntactic complexity, these results suggest that semantic indeterminacy is a major source of comprehension difficulty in object relative clauses. Results are consistent with constraint-based approaches to ambiguity resolution and bring new insights into previously identified sources of difficulty. (C) 2007 Elsevier Inc. All rights reserved

A Probabilistic Model of Semantic Plausibility in Sentence Processing

Experimental research shows that human sentence processing uses information from different levels of linguistic analysis, for example lexical and syntactic preferences as well as semantic plausibility. Existing computational models of human sentence processing, however, have focused primarily on lexico-syntactic factors. Those models that do account for semantic plausibility effects lack a general model of human plausibility intuitions at the sentence level. Within a probabilistic framework, we propose a widecoverage model that both assigns thematic roles to verb-argument pairs and determines a preferred interpretation by evaluating the plausibility of the resulting (verb,role,argument) triples. The model is trained on a corpus of role-annotated language data. We also present a transparent integration of the semantic model with an incremental probabilistic parser. We demonstrate that both the semantic plausibility model and the combined syntax/semantics model predict judgment and reading time data from the experimental literature. 1

Confidence in uncertainty: Error cost and commitment in early speech hypotheses

© 2018 Loth et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Interactions with artificial agents often lack immediacy because agents respond slower than their users expect. Automatic speech recognisers introduce this delay by analysing a user’s utterance only after it has been completed. Early, uncertain hypotheses of incremental speech recognisers can enable artificial agents to respond more timely. However, these hypotheses may change significantly with each update. Therefore, an already initiated action may turn into an error and invoke error cost. We investigated whether humans would use uncertain hypotheses for planning ahead and/or initiating their response. We designed a Ghost-in-the-Machine study in a bar scenario. A human participant controlled a bartending robot and perceived the scene only through its recognisers. The results showed that participants used uncertain hypotheses for selecting the best matching action. This is comparable to computing the utility of dialogue moves. Participants evaluated the available evidence and the error cost of their actions prior to initiating them. If the error cost was low, the participants initiated their response with only suggestive evidence. Otherwise, they waited for additional, more confident hypotheses if they still had time to do so. If there was time pressure but only little evidence, participants grounded their understanding with echo questions. These findings contribute to a psychologically plausible policy for human-robot interaction that enables artificial agents to respond more timely and socially appropriately under uncertainty