We demonstrate an integrated system for building and learning models and structures in both a real and virtual environment. The system combines natural language understanding, planning, and methods for composition of basic concepts into more complicated concepts. The user and the system interact via natural language to jointly plan and execute tasks involving building structures, with clarifications and demonstrations to teach the system along the way. We use the same architecture for building and simulating models of biology, demonstrating the general-purpose nature of the system where domain-specific knowledge is concentrated in sub-modules with the basic interaction remaining domain-independent. These capabilities are supported by our work on semantic parsing, which generates knowledge structures to be grounded in a physical representation, and composed with existing knowledge to create a dynamic plan for completing goals. Prior work on learning from natural language demonstrations enables learning of models from very few demonstrations, and features are extracted from definitions in natural language. We believe this architecture for interaction opens up a wide possibility of human-computer interaction and knowledge transfer through natural language.

This paper presents an analysis of a family of particular English constructions, all of which roughly express "purpose". In particular we look at the purpose clause, rationale clause, and infinitival relative clause. We (1) show that couching the analysis in a computational framework, specifically generation, provides a more satisfying account than analyses based strictly on descriptive linguistics, (2) describe an implementation of our analysis in the natural language generation system MUMBLE-86, and (3) discuss how our architecture improves upon the techniques used by other generation systems for handling these and other adjunct constructions.

We outline a model of generation with revision, focusing on improving textual coherence. We argue that high quality text is more easily produced by iteratively revising and regenerating, as people do, rather than by using an architecturally more complex single pass generator. As a general area of study, the revision process presents interesting problems: Recognition of flaws in text requires a descriptive theory of what constitutes well written prose and a parser which can build a representation in those terms. Improving text requires associating flaws with strategies for improvement. The strategies, in turn, need to know what adjustments to the decisions made during the initial generation will produce appropriate modifications to the text. We compare our treatment of revision with those of Mann and Moore (1981), Gabriel (1984), and Mann (1983).

Tree Adjoining Grammars, or "TAG's", (Joshi, Levy & Takahashi 1975; Joshi 1983; Kroch & Joshi 1985) were developed as an alternative to the standard syntactic formalisms that are used in theoretical analyses of language. They are attractive because they may provide just the aspects of context sensitive expressive power that actually appear in human languages while otherwise remaining context free.This paper describes how we have applied the theory of Tree Adjoining Grammars to natural language generation. We have been attracted to TAG's because their central operation---the extension of an "initial" phrase structure tree through the inclusion, at very specifically constrained locations, of one or more "auxiliary" trees---corresponds directly to certain central operations of our own, performance-oriented theory.We begin by briefly describing TAG's as a formalism for phrase structure in a competence theory, and summarize the points in the theory of TAG's that are germaine to our own theory. We then consider generally the position of a grammar within the generation process, introducing our use of TAG's through a contrast with how others have used systemic grammars. This takes us to the core results of our paper: using examples from our research with weil-written texts from newspapers, we walk through our TAG inspired treatments of raising and wh-movement, and show the correspondence of the TAG "adjunction" operation and our "attachment" process.In the final section we discuss extensions to the theory, motivated by the way we use the operation corresponding to TAG's adjunction in performance. This suggests that the competence theory of TAG's can be profitably projected to structures at the morphological level as well as the present syntactic level.

One of the key characteristics of any summary is that it must be concise. To achieve this the content of the summary (1) must be focused on the key events, and (2) should leave out any information that the audience can infer on their own. We have recently begun a project on summarizing simple narrative stories. In our approach, we assume that the focus of the story has already been determined and is explicity given in the story's long-term representation; we concentrate instead on how one can plan what inferences an audience will be able to make when they read a summary. Our conclusion is that one should think about inferences as following from the audience's recognition of the central concepts in the story's plot, and then plan the textual structure of the summary so as to reinforce that recognition.

An effective human-computer discourse system requires more than a clever grammar or a rich knowledge base. It needs knowledge about the user and his understanding of the domain in order to produce a relevant and coherent discourse. We describe MENO, a prototype tutor for elementary PASCAL, which uses a set of speech patterns modelled after complex human discourse and a richly annotated knowledge base to produce a flexible interactive system for the user.

We argue that in domains where a strong notion of salience can be defined, it can be used to provide: (1) an elegant solution to the selection problem, i.e. the problem of how to decide whether a given fact should or should not be mentioned in the text; and (2) a simple and direct control framework for the entire deep generation process, coordinating proposing, planning, and realization. (Deep generation involves reasoning about conceptual and rhetorical facts, as opposed to the narrowly linguistic reasoning that takes place during realization.) We report on an empirical study of salience in pictures of natural scenes, and its use in a computer program that generates descriptive paragraphs comparable to those produced by people.

Ultimately in any natural language production system the largest amount of human effort will go into the construction of the dictionary: the data base that associates objects and relations in the program's domain with the words and phrases that could be used to describe them. This paper describes a technique for basing the dictionary directly on the semantic abstraction network used for the domain knowledge itself, taking advantage of the inheritance and specialization machanisms of a network formalism such as KL-ONE. The technique creates considerable economics of scale, and makes possible the automatic description of individual objects according to their position in the semantic net. Furthermore, because the process of deciding what properties to use in an object's description is now given over to a common procedure, we can write general-purpose rules to, for example, avoid redundancy or grammatically awkward constructions.

Article Free Access Share on A linear-time model of language production: some psychological implications Author: David D. McDonald MIT Artificial Intelligence Laboratory, Cambridge, Massachusetts MIT Artificial Intelligence Laboratory, Cambridge, MassachusettsView Profile Authors Info & Claims ACL '80: Proceedings of the 18th annual meeting on Association for Computational LinguisticsJune 1980Pages 55–57https://doi.org/10.3115/981436.981454Published:19 June 1980Publication History 1citation196DownloadsMetricsTotal Citations1Total Downloads196Last 12 Months13Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF