It has been proposed that the design of robots might benefit from interactions that are similar to caregiver–child interactions, which is tailored to children’s respective capacities to a high degree. However, so far little is known about how people adapt their tutoring behaviour to robots and whether robots can evoke input that is similar to child-directed interaction. The paper presents detailed analyses of speakers’ linguistic behaviour and non-linguistic behaviour, such as action demonstration, in two comparable situations: In one experiment, parents described and explained to their nonverbal infants the use of certain everyday objects; in the other experiment, participants tutored a simulated robot on the same objects. The results, which show considerable differences between the two situations on almost all measures, are discussed in the light of the computer-as-social-actor paradigm and the register hypothesis. Keywords: child-directed speech (CDS); motherese; robotese; motionese; register theory; social communication; human–robot interaction (HRI); computers-as-social-actors; mindless transfer

To investigate the contributions of taggers or chunkers to the performance of a deep syntactic parser, Weighted Constraint Dependency Grammars have been extended to also take into consideration information from external sources. Using a weak information fusion scheme based on constraint optimization techniques, a parsing accuracy has been achieved which is comparable to other (stochastic) parsers.

Based on constraint optimization techniques, an architecture for robust parsing of natural language utterances has been developed. The resulting system is able to combine possibly contradicting evidence from a variety of information sources, using a plausibility-based arbitration procedure to derive fairly rich structural representations, comprising aspects of syntax, semantics and other description levels of language. The results of a series of experiments are reported which demonstrate the high potential for robust behaviour with respect to ungrammaticality, incomplete utterances, and temporal pressure.

Abstract. We present a parser for German that achieves a competitive accuracy on unrestricted input while maintaining a coverage of 100%. By writing well-formedness rules as declarative, defeasible constraints that integrate different sources of linguistic knowledge, very high robustness is achieved against all sorts of extragrammatical constructions.

We investigate the utility of supertag information for guiding an existing dependency parser of German. Using weighted constraints to integrate the additionally available information, the decision process of the parser is influenced by changing its preferences, without excluding alternative structural interpretations from being considered. The paper reports on a series of experiments using varying models of supertags that significantly increase the parsing accuracy. In addition, an upper bound on the accuracy that can be achieved with perfect supertags is estimated.