Pragmatic overloading in Natural Language instructions

It has long been noted that Natural Language utterances can communicate more than their conventional meaning (Grice, 1975). It has also been noted that behaving appropriately in response to instructions given in Natural Language requires understanding more than their conventional meaning This paper addresses one mechanism by which speakers convey, and hearers derive, such additional aspects of meaning { a mechanism we call pragmatic overloading. In pragmatic overloading, a clause interpreted as conveying directly or indirectly the goal of an action which is described by some other clause, forms the basis of constrained inference that leads to additional information about the action. The paper demonstrates pragmatic overloading through a variety of clausal adjuncts. We then discuss a framework that supports many of the inferences that pragmatic overloading gives rise to. This framework integrates a lexical semantics representation a la Jackendoo (1990) with a knowledge representation system, CLASSIC (Brachman et al., 1991), based on description logic. We give examples of its use, before concluding with a discussion of future work.

[1]  B. Levin Unaccusativity: At the Syntax-Lexical Semantics Interface , 1994 .

[2]  James F. Allen Towards a General Theory of Action and Time , 1984, Artif. Intell..

[3]  Barbara Di Eugenio,et al.  Free Adjuncts in Natural Language Instructions , 1990, COLING.

[4]  Barbara Di Eugenio,et al.  Understanding natural language instructions: a computational approach to purpose clauses , 1993 .

[5]  Gregory T. Stump The semantic variability of absolute constructions , 1984 .

[6]  S. Rosenschein,et al.  Computational Theories of Interaction and Agency , 1996 .

[7]  Mark Steedman,et al.  Dynamic Semantics for Tense and Aspect , 1995, IJCAI.

[8]  Norman I. Badler,et al.  Simulating humans: computer graphics animation and control , 1993 .

[9]  Robert M. MacGregor,et al.  Using a description classifier to enhance knowledge representation , 1991, IEEE Expert.

[10]  Peter F. Patel-Schneider,et al.  Living wiht Classic: When and How to Use a KL-ONE-Like Language , 1991, Principles of Semantic Networks.

[11]  Michael White,et al.  Conceptual Structures and CCG: Linking Theory and Incorporated Argument Adjuncts , 1992, COLING.

[12]  M ShieberStuart,et al.  The problem of logical-form equivalence , 1993 .

[13]  Norman I. Badler,et al.  Instructions, Intentions and Expectations , 1995, Artif. Intell..

[14]  Mark Steedman,et al.  Temporal Ontology and Temporal Reference , 1988, CL.

[15]  Patrick Suppes,et al.  Context-fixing semantics for the language of action , 1988 .

[16]  R. Jackendoff Parts and boundaries , 1991, Cognition.

[17]  Malka Rappaport Hovav,et al.  Wiping the slate clean: A lexical semantic exploration , 1991, Cognition.

[18]  Bonnie L. Webber,et al.  Instructing Animated Agents: Viewing Language in Behavioral Terms , 1995, Multimodal Human-Computer Communication.

[19]  Henry Kautz,et al.  A circumscriptive theory of plan recognition , 1990 .

[20]  M. Pollack Overloading Intentions for Eecient Practical Reasoning , 1991 .

[22]  Deirdre Wilson,et al.  Inference and Implicature , 1986 .

[23]  A. Goldman Theory of Human Action , 1970 .

[24]  Christopher W. Geib,et al.  The Intentional Planning System: ItPlanS , 1994, AIPS.

[25]  Z. Vendler Linguistics in Philosophy , 1967 .

[26]  Cecile Tiberghien Balkanski,et al.  Actions, beliefs and intentions in multi-action utterances , 1993 .

[27]  Douglas E. Appelt,et al.  Planning English Sentences , 1988, Cogn. Sci..

[28]  Christopher W. Geib,et al.  Planning for animation , 1996 .

[29]  John F. Sowa,et al.  Principles of semantic networks , 1991 .

[30]  Martha E. Pollack,et al.  Inferring domain plans in question-answering , 1986 .

[31]  Johanna D. Moore,et al.  A Problem for RST: The Need for Multi-Level Discourse Analysis , 1992, CL.

[32]  Norman I. Badler,et al.  Doing What You're Told: Following Task Instructions in Changing, but Hospitable Environments , 1992 .