Pitch accents create dissociable syntactic and semantic expectations during sentence processing

The language system uses syntactic, semantic, as well as prosodic cues to efficiently guide auditory sentence comprehension. Prosodic cues, such as pitch accents, can build expectations about upcoming sentence elements. This study investigates to what extent syntactic and semantic expectations generated by pitch accents can be dissociated and if so, which cues take precedence when contradictory information is present. We used sentences in which one out of two nominal constituents was placed in contrastive focus with a third one. All noun phrases carried overt syntactic information (case-marking of the determiner) and semantic information (typicality of the thematic role of the noun). Two experiments (a sentence comprehension and a sentence completion task) show that focus, marked by pitch accents, established expectations in both syntactic and semantic domains. However, only the syntactic expectations, when violated, were strong enough to interfere with sentence comprehension. Furthermore, when contradictory cues occurred in the same sentence, the local syntactic cue (case-marking) took precedence over the semantic cue (thematic role), and overwrote previous information cued by prosody. The findings indicate that during auditory sentence comprehension the processing system integrates different sources of information for argument role assignment, yet primarily relies on syntactic information

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

Cappadocian kinship

Cappadocian kinship systems are very interesting from a sociolinguistic and anthropological perspective because of the mixture of inherited Greek and borrowed Turkish kinship terms. Precisely because the number of Turkish kinship terms differs from one variety to another, it is necessary to talk about Cappadocian kinship systems in the plural rather than about the Cappadocian kinship system in the singular. Although reference will be made to other Cappadocian varieties, this paper will focus on the kinship systems of Mišotika and Aksenitika, the two Central Cappadocian dialects still spoken today in several communities in Greece. Particular attention will be given to the use of borrowed Turkish kinship terms, which sometimes seem to co-exist together with their inherited Greek counterparts, e.g. mána vs. néne ‘mother’, ailfó/aelfó vs. γardáš ‘brother’ etc. In the final part of the paper some kinship terms with obscure or hitherto unknown etymology will be discussed, e.g. káka ‘grandmother’, ižá ‘aunt’, lúva ‘uncle (father’s brother)’ etc

Attentive Speaking. From Listener Feedback to Interactive Adaptation

Buschmeier H. Attentive Speaking. From Listener Feedback to Interactive Adaptation. Bielefeld: Universität Bielefeld; 2018.Dialogue is an interactive endeavour in which participants jointly pursue the goal of reaching understanding. Since participants enter the interaction with their individual conceptualisation of the world and their idiosyncratic way of using language, understanding cannot, in general, be reached by exchanging messages that are encoded when speaking and decoded when listening. Instead, speakers need to design their communicative acts in such a way that listeners are likely able to infer what is meant. Listeners, in turn, need to provide evidence of their understanding in such a way that speakers can infer whether their communicative acts were successful. This is often an interactive and iterative process in which speakers and listeners work towards understanding by jointly coordinating their communicative acts through feedback and adaptation. Taking part in this interactive process requires dialogue participants to have ‘interactional intelligence’. This conceptualisation of dialogue is rather uncommon in formal or technical approaches to dialogue modelling. This thesis argues that it may, nevertheless, be a promising research direction for these fields, because it de-emphasises raw language processing performance and focusses on fundamental interaction skills. Interactionally intelligent artificial conversational agents may thus be able to reach understanding with their interlocutors by drawing upon such competences. This will likely make them more robust, more understandable, more helpful, more effective, and more human-like. This thesis develops conceptual and computational models of interactional intelligence for artificial conversational agents that are limited to (1) the speaking role, and (2) evidence of understanding in form of communicative listener feedback (short but expressive verbal/vocal signals, such as ‘okay’, ‘mhm’ and ‘huh’, head gestures, and gaze). This thesis argues that such ‘attentive speaker agents’ need to be able (1) to probabilistically reason about, infer, and represent their interlocutors’ listening related mental states (e.g., their degree of understanding), based on their interlocutors’ feedback behaviour; (2) to interactively adapt their language and behaviour such that their interlocutors’ needs, derived from the attributed mental states, are taken into account; and (3) to decide when they need feedback from their interlocutors and how they can elicit it using behavioural cues.This thesis describes computational models for these three processes, their integration in an incremental behaviour generation architecture for embodied conversational agents, and a semi-autonomous interaction study in which the resulting attentive speaker agent is evaluated. The evaluation finds that the computational models of attentive speaking developed in this thesis enable conversational agents to interactively reach understanding with their human interlocutors (through feedback and adaptation) and that these interlocutors are willing to provide natural communicative listener feedback to such an attentive speaker agent. The thesis shows that computationally modelling interactional intelligence is generally feasible, and thereby raises many new research questions and engineering problems in the interdisciplinary fields of dialogue and artificial conversational agents