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<Paper uid="P98-2146">
  <Title>Using Language Resources in an Intelligent Tutoring System for French</Title>
  <Section position="2" start_page="0" end_page="886" type="metho">
    <SectionTitle>
1 Artificial Intelligence
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="0" end_page="886" type="sub_section">
      <SectionTitle>
Language Learning
and
</SectionTitle>
      <Paragraph position="0"> Among the first milestones in Intelligent Tutoring Systems (ITS) was Carbonell's system (1970) that used a knowledge-base to check the student's answers and to allow him/her to interact in &amp;quot;natural language&amp;quot;. BUGGY, by Brown and Burton (1978) is another system more oriented towards student error diagnostic. At around the same period researchers were starting to put also some emphasis on the teaching strategies adopted in the system such as in WEST, Burton &amp; Brown (1976).</Paragraph>
      <Paragraph position="1"> It's with such works and many others later, that Intelligent Tutoring Systems' architecture was more or less separated into four modules: an expert's model, a learner's model, a teacher's model, and an interface, Wengers (1987).</Paragraph>
      <Paragraph position="2"> However, language learning had its own specific difficulties that were not generalized in other ITS systems. How to represent the linguistic knowledge in the expert and learner models? How to implement parsers that can process  ungrammatical input? How to implement teaching strategies that are appropriate for language learning? These are some of the issues of high interest, Chanier, Reni6 &amp; Fouquer6 (1993).</Paragraph>
      <Paragraph position="3"> Recent systems show how researchers are being more open to psycho linguistic, pedagogical and applied linguistic theories. For example, The ICICLE Project is based on L2 learning theory (McCoy et al., 1996); Alexia (Selva et al., 1997) and FLUENT (Hamburger and Hashim, 1992) are based on constructivism, Mr. Collins (Bull et al., 1995) is based on four empirical studies in an effort to &amp;quot;discover&amp;quot; student errors and their learning strategies.</Paragraph>
      <Paragraph position="4"> Another tendency, that is very noticeably parallel to that of NLP, is the development of sophisticated language resources such as dictionaries for language (lexical) learning as exemplified by CELINE at Grenoble (Men6zo et al., 1996), the SAFRAN project (1997) and The Reader at Princeton University (1997) which uses WordNet, or real corpuses as in the European project Camille (Ingraham et al., 1994).</Paragraph>
      <Paragraph position="5"> The literature review lead us to believe in the following basic principles: P1. Language is learned in context through communication and experience, Chanier (1994).</Paragraph>
      <Paragraph position="6"> P2. Language is learned in the natural order from receptive to productive.</Paragraph>
      <Paragraph position="7"> P3. Grammatical forms ought to be taught through language patterns.</Paragraph>
      <Paragraph position="8"> P4. Vocabulary learning means learning the words and their limitations, probability of occurrences, and syntactic behavior around them, Swartz &amp; Yazdani (1992).</Paragraph>
    </Section>
  </Section>
  <Section position="3" start_page="886" end_page="886" type="metho">
    <SectionTitle>
2 An Empirical Study for
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="886" end_page="886" type="sub_section">
      <SectionTitle>
Learner Model
</SectionTitle>
      <Paragraph position="0"> In an effort to gain some insight into the projected linguistic model, an empirical study on the population of elementary students in the City of Moncton, New Brunswick, Canada was completed 1. The study consisted of one-on-one interviews where the children were presented with images having very few possible This work was done by A. S. Picolet-Cr6pault within her PhD thesis.</Paragraph>
      <Paragraph position="1"> interpretations. The only question that was asked was &amp;quot;Qu'est-ce que c'est?&amp;quot; (What is this?). In the next sections, we will examine the children's answers concerning relative clauses.</Paragraph>
    </Section>
    <Section position="2" start_page="886" end_page="886" type="sub_section">
      <SectionTitle>
2.1 Subject Relative Clauses
</SectionTitle>
      <Paragraph position="0"> When the children were asked about the main subject in the picture, the answers were acceptable in standard French, showing that they had no problems in using relative clauses with qui. Following are some examples:  I. C'est une chienne qui boit; 2. C'est un chien qui boit du iait; Some of the answers showed other elements concerning lexical use: 3. C'est un gargon qui kick la balle.</Paragraph>
      <Paragraph position="1"> (Use of an English verb) 4. C'est une fiile qui botte le ballon. (Use of an inappropriate verb) 5. C'est un papa etson garqon.</Paragraph>
    </Section>
    <Section position="3" start_page="886" end_page="886" type="sub_section">
      <SectionTitle>
(Bypassing strategy)
2.2 Object Relative Clauses
</SectionTitle>
      <Paragraph position="0"> In this part of the experiment, the object of the picture was the center of the questions.</Paragraph>
      <Paragraph position="1"> Following are some of the answers with the most frequent errors or bypassing strategies, they are marked with a *; the sentences with italics are the acceptable ones:  6. C'est le livre que le garcon lit.</Paragraph>
      <Paragraph position="2"> *7. C'est le livre qui se fait lire par la fille. *8. C'est le livre h la fille.</Paragraph>
      <Paragraph position="3"> *9. C'est le iivre qu'elle lit dedans.</Paragraph>
      <Paragraph position="4"> *10. C'est un livre, la fille lit le livre.  The errors seen in these examples constitute around fifty percent of the answers given by first grade children and are reduced to around thirty percent in sixth grade. Answers 7 and 10 are examples of bypassing strategies i.e.; the use of a different verb or another sentence structure as a means for avoiding relative clauses.</Paragraph>
      <Paragraph position="5"> Answer 8 shows a common use of the preposition h instead of de. Answer 9 is also representative of the frequent use of prepositions at the end of the sentence.</Paragraph>
    </Section>
    <Section position="4" start_page="886" end_page="886" type="sub_section">
      <SectionTitle>
2.3 Complex Relative Clauses
</SectionTitle>
      <Paragraph position="0"> The following examples give a brief survey of the use of indirect object relative clauses: avec lequel / laquelle, sur lequel / laquelle, ~ qui,  and dont: 11. C'est le crayon avec lequel elle 6crit. * 12. C'est le crayon qui ~crit.</Paragraph>
      <Paragraph position="1"> * 13. C'est le crayon qu'il se sert pour ses devoirs. 887 14. C'est la branche sur laquelle est l'oiseau &amp;quot;15. C'est une branche que l'oiseau chante sur. &amp;quot;16. C'est une branche que I'oiseau est assis. 17. C'est le garqon ~ qui le monsieur parle. * 18. C'est le garqon qui s'assoit sur une chaise. &amp;quot;19. C'est le garqon que le monsieur parle. 20. C'est la maison dont la femme rSve.</Paragraph>
      <Paragraph position="2"> *21. C'est la maison que la dame rSve.</Paragraph>
      <Paragraph position="3"> *22. C'est la maison que la madame rSve de.</Paragraph>
    </Section>
    <Section position="5" start_page="886" end_page="886" type="sub_section">
      <SectionTitle>
2.4 Error Summary
</SectionTitle>
      <Paragraph position="0"> By looking at these examples, it is evident that complex relative clauses are rather unknown to the children. They show that the easiest particles for them are qui and que even when misused as in answer 12.</Paragraph>
      <Paragraph position="1"> It can also be concluded that they use que in a non standard manner every time they need to use complex relative clauses. Otherwise they use a bypassing strategy by separating the sentence into two parts as in &amp;quot;C'est une branche et un oiseau&amp;quot;, or by using another verb that allows qui as in 18.</Paragraph>
    </Section>
  </Section>
  <Section position="4" start_page="886" end_page="886" type="metho">
    <SectionTitle>
3 General System Overview
</SectionTitle>
    <Paragraph position="0"> The system we are building has a mixed initiative, multi-agent architecture. Mixed initiative because we want the system to serve both the teacher and the student, in both teaching and in learning modes. For example, the teacher could favor certain activities such as presenting examples of &amp;quot;non standard French sentences&amp;quot; and opposing them to English structures in a effort to show the children some Anglicisms; or maybe choose a specific microworld, such as Holloween or Christmas so that the exercises would be closer to children's real daily experience (principle P1).</Paragraph>
    <Paragraph position="1"> The syntactic graph and the lexicon are annotated with probabilities on usually faulty expressions in order to intensify the explanation or the number of examples and exercises on those particular parts (principles P3 and P4).</Paragraph>
    <Paragraph position="2"> We do not intend to build a fully free learning environment. The environment is partially structured. The user chooses where to start by clicking on a hot-button picture. He/she chooses the micro-domain and the wanted activities.</Paragraph>
    <Paragraph position="3"> However, unexpected &amp;quot;pop-up&amp;quot; activities would come up on the screen from time to time (style&amp;quot; Tip of the day&amp;quot; or &amp;quot;TV ad.&amp;quot;).</Paragraph>
    <Paragraph position="4"> As this system is being built for young children,  not every single word is expected to be typed on the keyboard. Following are some examples of the look and feel of our system: 1. Children can pick activities from graphical images on the screen.</Paragraph>
    <Paragraph position="5"> 2. Corpuses or extracts from children stories are equipped with hyperlinks to word meanings or grammar usage explanations.</Paragraph>
    <Paragraph position="6"> 3. Puzzle playing where words have assigned shapes according to their functions. Fitting the puzzle means placing the words in the correct order.</Paragraph>
    <Paragraph position="7"> 4. Picking words they like and asking the system  to make up a sentence; All the above possibilities are optional. This allows the teacher to take responsibility of the degree of unstructured or of focused learning.</Paragraph>
  </Section>
  <Section position="5" start_page="886" end_page="889" type="metho">
    <SectionTitle>
4 GETA's Used Resources
</SectionTitle>
    <Paragraph position="0"> For many years GETA has been working on MT systems from and into French. An impressive core of linguistic knowledge is available but has not yet been experimented on in building language learning software, though work is underway for integration of heterogeneous NLP components, Boitet &amp; Seligman (1994). Ariane for example, uses special purpose rule-writing formalisms for each of its morphological and lexical modules both for analysis and for generation, with a strict separation of algorithmic and linguistic knowledge, Hutchins &amp; Somers (1992).</Paragraph>
    <Paragraph position="1"> The following modules from GETA were used in our experiment 2 : A. Morphological agent.</Paragraph>
    <Paragraph position="2"> -ATEF for the morphological analysis subagent. null -SYGMOR for the morphological generation sub-agent.</Paragraph>
    <Paragraph position="3"> B. Lexical agent.</Paragraph>
    <Paragraph position="4"> -EXPANSF for lexical expansion -TRANSF for translation into standard French C. ROBRA in its multi-level analysis -for syntactic tree definitions and  The first series of experiments we realized using GETA's resources concentrate on double analysis/generation of standard French and non-standard local French . The corpus consisted of the sentences collected during the empirical study (see section 2).</Paragraph>
    <Paragraph position="5"> Figures 1 and 2 show an example of the annotated trees created by Ariane during this C'est la maison que la dame r~ve de</Paragraph>
    <Paragraph position="7"> double generation of Acadian French and Standard French.</Paragraph>
    <Paragraph position="8"> These two graphs show how straight forward was the use of language resources for highlighting similarities and/or differences in these two dialects. Tha same grammar can be used by incrementing its rules to include new/different sentence structures. The lexicon can be augmented similarly.</Paragraph>
    <Paragraph position="9"> fs(gov) cat(d~~) fs(des) cat(n) fs(gov) cat v~.~,(~,~ fs(gov) ~ fs(reg) ) cat(s) Figure \]: Annotated tree for a sentence in non-standard French. C'est la maison dont la dame r&amp;ve</Paragraph>
    <Paragraph position="11"> Another alternative would be to consider the non-standard French as a completely new language from all points of view. In this case only the formalisms at GETA would be exploited not the existing linguistic data.</Paragraph>
    <Paragraph position="12"> Conclusion We have presented in this paper an ongoing software development project that is still in its early phases. In the introduction and in the first sections, we have argued for the positive effects of computers on language learning and then on some of the issues that researchers in the field are hoping to see implemented from a computational and a pedagogical point of view. We have also seen, through an empirical study, the kinds of linguistic difficulties that a minority group is encountering. In such a case one cannot help but to think about the advantages that technology can offer, especially in an era where Language resources are ready for the pick. We have opted to use the highly formalized and parameterized resources at GETA in an effort to develop a quickly functional prototype that we can immediately submit for on-the ground testing.</Paragraph>
  </Section>
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