Language Learning as Problem Solving

By M. Zock, Gil Francopoulo, Abdellatif Laroui. 1988

We present here a system under development, the present goals of which are to assist (a) students in inductively learning a set of rules to generate sentences in French, and (b) psychologists in gathering data on natural language learning. Instead of claimin~ an all-encompassing model or theory, we prefer to elaborate a tool, which is general and flexible enough to permit the testing of various theories. By controlling parameters such as initml knowledge, the nature and order o f the data, we can empirically determine how each parameter affects the efficiency of learning. Our ultimate goal is the modelling of human learning by machine. Learning is viewed as problem-solving, i.e. as the creation and reduction of a search-space, t~y integrating the student into the process, that is, by encouraging him to ask an expert (the system) certain kinds of questions like: can one say x ? how does one say x ? why does one say x ? we can ennance not only the efficiency of the learning, but also our understanding of the underlying processes. By having a tra.~e of the whole dialogue (what questions have been asked at, what time), Ave should be able to infer the student's learning strategies. I THE PROBLEM OF LEARNING A LANGUAGE: Language learning can be viewed as a special case of problem solving in which tlae learner tries to build and intelligently explore a hypothetical search space. If this view is correct, then two sets of questions arise immediately. On one hand one may want to know: a) what the nature of this search space is (what are the variables 9) b) how it is built (incremental learning: local vg global view), ' ' c) how it is explored (strategies: intelligent opportunistics vs systematic search). On the other hand, one may want to investigate how (i) the knowledge at the outset and (ii) the ordering of the data will affect the building and the searching of the space. Typically one does not learn from scratch, nor is it likely that one encounters either well-ordered data, or a Complete set of examples: natural learning is incremental. Obviously, these.facts imply that: * initial knowledge, in particular, knowledge of other languages may bias the kind of variables (attributes or hypotheses) constdered, i.e., included in the search space; * the order of the data (the examp es encountered by the student) may determine what rules are likely to be inferred at what moment, and finally rues are referred from mcomplete data (incremental learning). Furthermore, the same data may be characterized in different ways. That is, several equivalent descriptions may be inferred from the same data set. Whieli of these descriptions turns out to be the most adequate generally cannot be established until one knows the complete data set. Thus, rules may have to be revised in the light of new evidence. Consequently, errors are not only unavoidable parts of the learning process,but also an indispensable source of information for the learner. 2 THE PROBLEM OF TEACHING HOW TO LEARNt As we have shown, learning can be seen as searching. Actually, teaching, as well as learning, can be conceived of as problem solving or reasoning in an informatio.n-exchange environment. There is a sender, a goal, a message and a recewer. The SENDER may be a native speaker, a teacher, a parent, a book or a computer. The GOAL is the task or performance (output). In our case it is knowledge of how to produce sentences in French. The MESSAGE is the input to the learning component: examples from which the rules have to be inferred (1). The RECEIVER or learner can be any system, naturm or artlttcial, capable of perceiving, memorizing and analyzing a set of data and drawing the necessary conclusions: a child, a student, or a computer program (2). Learning occurs in various settings. Depending on the order of the examples and the control of the information flow we s eak of nator I . . . . . . p a experimental, or msmuttonal settings. Natural learning is characterized by the absence of a clearly defined learning objective (3), by noisy and heterogeneous material~ and by unordered examples. The underlying regumr:ties are thus multiple, diffuse, and hard to perceive. Experimentffl learning and teaching, on the other hand, have a ]earning objective, the material is error-free, homogeneous and coherently ordered according to some point of view (learner or teacher). Whereas experimental learning can be characterized by the following sequence: (i) encountering the data (ii) analysis, (iii) building and testing of h~,pothesis, (iv) feedback and (v) proof or aemonstration of the theo~, traditional teaching goes througfi the following stages: (i) exposition, 0i) practice, (iii) testing and (iv) evaluation. This can be schematized as follows: Teacher: sets the task and presents the learning material Student: analyzes the data; Teacher: provtdes a set of examples; Student: practices; Teacher: asks questions to test the gained knowledge; Student: answers the questions; Teacher: evaluates the answers, provides feedback (explanations) and organizes future data as a function of actual performance Student: integrates the feedback into the knowledge base and corrects misconceptions; As one can see, the information flow here is entirely teacher-controlled. He is the one who sets the task, and provides the examples and the feedback. Consequently, the teacher decides the nature and the order of the material to be learned. There are two major shortcomings in this approach. Not knowiug what information is needed by the learner, the teacher may present the wrong data. More importantly, the student is only loosely integrated in the learning process. Instead of being active, generating and testing plausible hypotheses (discovery learning), he reacts to questions, Thus, it may happen that the student perceives his task as the learning of the material rather than the learning of the underlying principles. I~norance of what or how to learn may result in (i) learning the unintended 0:) poor problem-solving skills or (iii) little transfer. As long as the learne~" does not go beyond the informaBon given (the concrete word level), he cannot transfer the gained knowledge to similar situations, because the perception of similarity presupposes abstraction. Given these criticisms, it would be useful to have a system which has the qualities mentioned above without having the drawbacks. A good learning environment should be both flexible and constraining enough: * to allow for simulation of real Communication, that is to say, to provide a setting where both participants can take the initiative and control the information-flow, * O " " t ensure the learmng of the appropriate material (i.e., what to learn) as well as the necessary problem-solving skills (the methods, i.e, how to learn). A computerprogram could provide such an environment. It would offer different k!nds oi' !nformation (see below: trace-function), while answering me stuuent's questions as ne goes aJong generating and testing different sorts of hypotheses. 3 THE COGNITIVE ENGENEER'S TASK: to provide the user a friendly interface We will describe here a system under development, whose major goals are: * O ' " " t provide an environment whtch allows communication between a learner (student) and an expert (in our case the system); * O " ' " ' t s~mulate the mformatton-processmg aspect of natural learning, i.e., the inductive learning of grammatical rules to generate sentences in French. * to allow teachers and psychologists to test various theories.

Subfields: Cognitive Science & Psychology, Linguistics