TY  - CHAP
A1  - DeVault, David
A1  - Stone, Matthew
T1  - Scorekeeping in an uncertain language game
N2  - Received views of utterance context in pragmatic theory characterize the occurrent subjective states of interlocutors using notions like common knowledge or mutual belief. We argue that these views are not compatible with the uncertainty and robustness of context-dependence in human–human dialogue. We present an alternative characterization of utterance context as objective and normative. This view reconciles the need for uncertainty with received intuitions about coordination and meaning in context, and can directly inform computational approaches to dialogue.
Y1  - 2006
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-10448
ER  - 
TY  - CHAP
A1  - Lascarides, Alex
A1  - Stone, Matthew
T1  - Formal semantics for iconic gesture
N2  - We present a formal analysis of iconic coverbal gesture. Our model describes the incomplete meaning of gesture that’s derivable from its form, and the pragmatic reasoning that yields a more specific interpretation. Our formalism builds on established models of discourse interpretation to capture key insights from the descriptive literature on gesture: synchronous speech and gesture express a single thought, but while the form of iconic gesture is an important clue to its interpretation, the content of gesture can be resolved only by linking it to its context.
Y1  - 2006
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-10330
ER  - 
TY  - CHAP
A1  - Dzikovska, Myroslava O.
A1  - Callaway, Charles B.
A1  - Stone, Matthew
A1  - Moore, Johanna D.
T1  - Understanding student input for tutorial dialogue in procedural domains
N2  - We present an analysis of student language input in a corpus of tutoring dialogue in the domain of symbolic differentiation. Our focus on procedural tutoring makes the dialogue comparable to collaborative problem-solving (CPS). Existing CPS models describe the process of negotiating plans and goals, which also fits procedural tutoring. However, we provide a classification of student utterances and corpus annotation which shows that approximately 28% of non-trivial student language in this corpus is not accounted for by existing models, and addresses other functions, such as evaluating past actions or correcting mistakes. Our analysis can be used as a foundation for improving models of tutoring dialogue.
Y1  - 2006
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-10193
ER  -