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<Paper uid="P06-3001">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Flexible Approach to Natural Language Generation for Disabled Children</Title>
  <Section position="3" start_page="0" end_page="1" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> 'Natural Language Generation' also known as 'Automated Discourse Generation' or simply 'Text Generation', is a branch of computational linguistics, which deals with automatic generation of text in natural human language by the machine. It can be conceptualized as a process leading from a high level communicative goal to a sequence of communicative acts that accomplish this communicative goal (Rambow et. al., 2001). Based on input representation, any NLG technique can be broadly classified into two paradigms viz. Template based Approach and Plan based approach. The template-based approach does not need large linguistic knowledge resource but it cannot provide the expressiveness or flexibility needed for many real domains (Langkilde and Knight, 1998). In (Deemter et.</Paragraph>
    <Paragraph position="1"> al., 1999), it has been tried to prove with the example of a system (D2S: Direct to Speech) that both of the approaches are equally powerful and theoretically well founded. The D2S system uses a tree structured template organization that resembles Tag Adjoining Grammar (TAG) structure. The template-based approach that has been taken in the system, enables the basic language generation algorithms application independent and language independent. At the final stage of language generation it checks the compatibility of the sentence structure with the current context and validates the result with Chomsky's binding theory. For this reason it is claimed to be as well founded as any plan-based approach. As another practical example of NLG technique, we can consider the IBM MASTOR system (Liu et. al., 2003). It is used as speech-to-speech translator between English and Mandarin Chinese. The NLG part of this system uses trigram language model for selecting appropriate inflectional form for target language generation.</Paragraph>
    <Paragraph position="2"> When NLG (or NLP) technology is applied in assistive technology, the focus is shifted to increase communication rate rather than increasing the efficiency of input representation.</Paragraph>
    <Paragraph position="3"> As for example, CHAT (Alm, 1992) software is an attempt to develop a predictive conversation model to achieve higher communication rate during conversation. This software predicts different sentences depending on situation and mood of the user. The user is free to change the situation or mood with a few keystrokes. In &amp;quot;Compansion&amp;quot; project (McCoy, 1997), a novel approach was taken to enhance the communication rate.</Paragraph>
    <Paragraph position="4"> The system takes telegraphic message as input and automatically produces grammatically correct sentences as output based on NLP techniques. The KOMBE Project (Pasero, 1994) tries to enhance the communication rate in a different way. It predicts a sentence or a set of sentence by taking sequence of words from users. The Sanyog project (Sanyog, 2006)(Banerjee, 2005) initiates a dialog with the users to take different portions (eg. Subject, verb, predicate etc.) of a sentence and automatically constructs a grammatically correct sentence based on NLG techniques. null</Paragraph>
  </Section>
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