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<Paper uid="W00-0711">
  <Title>Learnability: A Self-contained Tutorial for</Title>
  <Section position="6" start_page="64" end_page="65" type="concl">
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4 Discussion
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    <Paragraph position="0"> Although presented as a feasibility analysis of parameter-setting -- specifically of STL performance, it should be clear that the relevant factors e', e, r, etc. can be applied to shape an abstract input domain for almost any learning strategy. This is important because questions of a model's feasibility have proved difficult to answer in spaces of a linguistically plausible size.</Paragraph>
    <Paragraph position="1"> Recent attempts necessarily rely on severely small, highly circumscribed language domains (e.g. Gibson and Wexler (1994), among others).</Paragraph>
    <Paragraph position="2"> These studies frequently involve the construction of an idealized language sample which is (at best) an accurate subset of sentences that a child might hear. A simulated learner is let loose on the input space and results consist of either the structure of the grammar(s) acquired or the specific circumstances under which the learner succeeds or fails to attain the target.</Paragraph>
    <Paragraph position="3"> Without question, this research agenda is valuable and can bring to light interesting characteristics of the acquisition process. (cf. Gibson and Wexler's (1994) argument for certain default parameter values based on the potential success or failure of verb-second acquisition in a three-parameter domain. And, for a different perspective, Elman et al.'s (1996) discussions of  English part-of-speech and past-tense morphology acquisition in a connectionist framework.) I stress that my point here is not to give a full accounting of STL performance. Substantim work has been completed towards this end (Sakas (2000), Sakas and Fodor (In press.)), as well as development of a similar framework to other models (See Sakas and Demner-Fushman (In prep.) for an application to Gibson and Wexler's Triggering Learning Algorithm). Rather, I intend to put forth the conjecture that syntax acquisition is extremely sensirive to the distribution of ambiguity, and, given this extreme sensitivity, suggest that simulation studies need to be conducted in conjunction with a broader analysis which abstracts away from whatever linguistic particulars are necessary to bring about the sentences required to build the input sample that feeds the simulated learner.</Paragraph>
    <Paragraph position="4"> Ultimately, whether a particular acquisition model is successful is an empirical issue and depends on the exact conditions under which the model performs well and the extent to which those favorable conditions are in line with the facts of human language. Thus, I believe a three-fold approach to validate a computational model of acquisition is warranted. First, an abstract analysis (similar to the one presented here) should be constructed that can be used to uncover a model's sweet spots -- where the shape of ambiguity is favorable to learning performance. Second, a computational psycholinguistic study should be undertaken to see if the model's sweet spots are in line with the distribution of ambiguity in natural language. And finally, a simulation should be carried out.</Paragraph>
    <Paragraph position="5"> Obviously, this a huge proposal requiring years of person-hours and coordinated planning among researchers with diverse skills. But if computational modeling is going to eventually lay claim to a model which accurately mirrors the human process of language acquisition, years of fine grinding are necessary.</Paragraph>
    <Paragraph position="6"> Acknowledgments. This work was supported in part by PSC-CUNY-30 Research Grant 6159500-30. The three-fold approach is at the root of a project we have begun at The City University of New York. Much thanks to my collaborators Janet Dean Fodor and Virginia Teller for many useful discussions and input, as well as to two anonymous reviewers for their helpful comments.</Paragraph>
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