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<Paper uid="C86-1134">
  <Title>LOC RED DEEP CASES LOCATIVE? DIRECTION?, LOCATIVE? DIRECTION? LOCATIVE? NIL NIL LOCATIVE? DIRECTION?, LOCATIVE? LOCATIVE? NIL DIRECTION? LOCATIVE? LOCATIVE? SOURCE?, DIRECTION? NIL</Title>
  <Section position="8" start_page="574" end_page="574" type="concl">
    <SectionTitle>
6 Conclusion and Related Research
</SectionTitle>
    <Paragraph position="0"> We have proposed the scheme of anticipated visualization to generate coherent texts describing reaL-wnrld events (visual data).</Paragraph>
    <Paragraph position="1"> The selection algorithms are based on low-level, verbinherent pro.perties, and on a pragmatically motivated verb hierarchy. 'lk~gether with t, he verbalization component the NAOS system is now fully operational from event, recognition to text generation in the domain of trafl'ie scenes. As this domain is rich enough to still pose a 1ol; of problems I, his opens up l, he ol)portunity t,o inl;egral;e higher level sl, rabelJies for e.g. combining sentences, selecting evengs, generating deie~ie expressions, el;e.</Paragraph>
    <Paragraph position="2"> The main difference between NAOS and other systems for language generation is that, we approach the verbalization problem from the visual side. and thus are led to use basic selection algoril;hms. Other systems like TAI,ESI'iN \[151, KI)S \[12J, TEXT \[1,t, KAMI' \[l\], and I1AM-ANS \[1()} start their proeessi,g wibh language whereas NAOS starts with images. In close emmection to our resea,&lt;, is U,e wo,'k ,,f \[21, 1~,4}, 1231, \[??,\], ~,.,,d \[,% 'rhe fi,.st iV)u,. authors deal wilJl questions of moqon recognition and with a re-.</Paragraph>
    <Paragraph position="3"> ferellcc senlant, ic for irlOt;iOrl verbs })Lit ~Ll'e IIot. CoLleerlled wit}l i, exL general~ion. They showed that case frames can Iw used to generate single utl,erancem Conklin and Ivh:l)onald use the notion of salience to deal wil, h ghe seleel,ion problem in the task of describing a single image of a nal)ural oul, door scene.</Paragraph>
    <Paragraph position="4"> TALESPIN exemplifies ~ha~; plans and goals of an actor may form the underlying sl, rueture of narratives and may I;hus be motivation for l;ext generation, hi KI)S a represental, ion of wha~ to do in ea~(., of fire alarm is transformed into a natural language.</Paragraph>
    <Paragraph position="5"> text. As the initial representa1,ion already contains lexieal eni, ries and primitive l)roposilfions the task is to organize tJds information anew so that i~ may be expressed ill an English text. Matll/ and Moore prol)ose rules for (:oml)ining l)ropositiolm and re,.ediL the text eonl, inuously to produce l,he final version. TEXT gem.'rate~; pars.</Paragraph>
    <Paragraph position="6"> gr~tplls as aiiswel's ~o qtlestiolls a\[)ollt da\[,abase Stl'llCtl/Fe. \[~e/cl(ef)wI1 }las idenl;ified discourse stra(.e, gie,~ for fulfilling three (;(mmmlaie~fl.ive goals: detine, compare, aud describe. These sl, rategi(~s g,dde, the t,;eaeration l)ro(:e.&lt;ls ill deciding what; to say \]lext. Me}(,eowlI Ilses 1,he qucsl;ion to deteemine tile eommunh:al.ive goal that the text should fldfil. Research of IJfis kind is very important to clarify ~he relation between l, he \[orln of (-z text and il;s underlying goals.</Paragraph>
    <Paragraph position="7"> ()ue of I;he domains of IIAM..ANS is the Mad of I;raflic scene which is also used in NAOS. /n this domain I|AM-ANS deals with primarily with answering questions about ~he tool, iota; o\[ ol@~cts and wi~h overanswering yes/no que,%ions \[25 I. The dialogue (:orei)onent, of IIAM-ANS may be commcted to NA()~g I;o also allow quest,ions of the user if' t}m generated text was not sM\[ieienl fi)r his underst;anding. An evalual;ion of the kind of question being asked by a user may help in devising bel, ter generation strategies.</Paragraph>
    <Paragraph position="8"> |(AMP is a, system tbr plamfing natural languago ubteranees ia the domain of task oriented dialogues. The 1)lantfinlg altorithm i;akes 1;he knowledge and I)elief'a of the hearer into account,. '\['his sy.stem shows |low a priori beliefs of 1;he hearer may a\]L;o be integrated in NAOS to generat;e appropria/;e referring phrases.</Paragraph>
    <Paragraph position="9"> It would be interesting to use a phrasing componen~ for NAOS which would firs/, determine all deep eases uecessary ~o maximally restrict \[,he visualized t, ra.jeet;ory of an objeet's mot, ion sequence and then try to distribute I;he cases to the di\[ferent verbs u.sed in the descripl;ion in order to general;e smooth text.</Paragraph>
  </Section>
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