<?xml version="1.0" standalone="yes"?>
<Paper uid="P86-1015">
  <Title>A MODEL OF REVISION IN NATURAL LANGUAGE GENERATION</Title>
  <Section position="2" start_page="0" end_page="90" type="intro">
    <SectionTitle>
1. INTRODUCTION
</SectionTitle>
    <Paragraph position="0"> / Revision is a large part of the writing process for people. This is one respect in which writing differs from speech. In ordinary conversation we do not rehearse what we are going to say; however, when writing a text which may be used more than once by an audience which is not present, we use a multipass system of writing and rewriting to produce optimal text. By reading what we write, we seem better able to detect flaws in the text and see new options for improvement.</Paragraph>
    <Paragraph position="1"> Why most people are not able to produce optimal text in one pass is an open and interesting question. Flower and Hayes (1980) and Collins and Gentner (1980) suggest that writers are unable to juggle the excessive number of simultaneous demands and constraints which arise in producing well written text. Writers must concentrate not only on expressing content and purpose, but also on the discourse conventions of written prose: the constraints on sentence, paragraph, and text structure which are designed to make texts more readable. Successive iterations of writing and revising may allow the writer to reduce the number of considerations demanding attention at a given time.</Paragraph>
    <Paragraph position="2"> The developers of natural language generation systems must also address the problem of how to produce high quality text. Most systems today concentrate on the production of dialogs or commentaries, where the texts are generally short and the coherence is strengthened by nonlinguistic context. However, in written documents coherence must be maintained by the text alone. In addition, written text must anticipate the questions of its readers. The text must be clear and well organized so that the reader may follow the points easily, and it must be concise and interesting so as to hold the reader's attention. These considerations place greater demands on a generation system.</Paragraph>
    <Paragraph position="3"> Most natural language generation systems generate in a single pass with no revision. A drawback of this approach is that the information necessary for decision making must be structured so that at any given point the generator has enough information to make an optimal decision. While many decisions require only local information, decisions involving long range dependencies, such as maintaining coherence, may require not only a history of the decisions made so far, but also predictions of what future decisions might be made and the interactions between those decisions.</Paragraph>
    <Paragraph position="4"> An alternative approach is a single pass system which incorporates provisions for revision of its internal representations at specific points in the generation process (Mann &amp; Moore, 1981; Gabriel, 1984). Evaluating the result of a set of decisions after they have been made allows a more parsimonious distribution of knowledge since specific  types of improvements may be evaluated at different stages. Interactions among the decisions made so far may also be evaluated rather than predicted. The problem remains, however, of not being able to take into account the interaction with future decisions.</Paragraph>
    <Paragraph position="5"> A third approach, and the one described in this paper, is to use the writing process as a model and to improve the text in successive passes. A generation/revision system would include a generator, a parser, and an evaluation component which would assess the parse of what the generator had produced and determine strategies for improvement. Such a system would be able to tailor the degree of refinement to the particular context and audience. In an interactive situation the system may make no refinements at all, as in &amp;quot;off the cuff&amp;quot; speech; when writing a final report, where the quality of the text is more important than the speed of production, it may generate several drafts.</Paragraph>
    <Paragraph position="6"> While single pass approaches may be engineered to give them the ability to produce high quality text, the parser-mediated revision approach has several advantages. Using revision can reduce the structural demands on the generator's representations, and thus reduce the overall complexity of the system. Since the revision component is analyzing actual text with a parser, it can assess long range dependencies naturally without needing to keep a history within the generator or having it predict what decisions it might make later.</Paragraph>
    <Paragraph position="7"> Revision also creates an interesting research context for examining both computational and psychological issues. In a closed loop system, the generator and parser must interact closely. This provides an opportunity to examine how these processes differ and what knowledge may be shared between them. In a similar vein, we may use a computational model of the revision task to assess the computational implications of proposed psychological theories of the writing process.</Paragraph>
  </Section>
class="xml-element"></Paper>