Learning Multiple Languages in Groups

We consider a variant of Gold’s learning paradigm where a learner receives as input n different languages (in form of one text where all input languages are interleaved). Our goal is to explore the situation when a more “coarse” classification of input languages is possible, whereas more refined classification is not. More specifically, we answer the following question: under which conditions, a learner, being fed n different languages, can produce m grammars covering all input languages, but cannot produce k grammars covering input languages for any k > m. We also consider a variant of this task, where each of the output grammars may not cover more than r input languages. Our main results indicate that the major factor affecting classification capabilities is the difference n − m between the number n of input languages and the number m of output grammars. We also explore relationship between classification capabilities for smaller and larger groups of input langu...

• Type: Conference
• Year: 2005
• Where: ALT
• Authors: Sanjay Jain, Efim B. Kinber
• Keywords: ALT 2005, Grammars Covering Input, Input Languages, Machine Learning, Output Grammars