Finite Sentence-Interface Control for Learning Bounded-Fan-Out Linear MCFGs under Fixed Monoid Typing

TL;DR

This paper introduces a finite sentence-interface control method enabling the exact learning of bounded-fan-out linear MCFGs from positive data, extending context-free grammar learning capabilities.

Contribution

It develops a typed refinement and characteristic sample approach for fixed fan-out and monoid homomorphism, ensuring polynomial-time learnability of these grammars.

Findings

The class of bounded-fan-out linear MCFGs is identifiable in the limit from positive data.

A finite characteristic sample can be constructed for exact language reconstruction.

Hypotheses from finite samples are computable in polynomial time.

Abstract

We study positive-data learning of bounded-fan-out linear multiple context-free grammars under a fixed explicit finite monoid homomorphism \(h\). The main obstacle beyond the context-free case is that an MCFG nonterminal derives a tuple whose components may be placed in a surrounding sentence in different orders. We introduce sentence-interface types as finite external control objects for such tuple occurrences. A type records the permutation of tuple components in the final sentence together with the \(h\)-values of the boundary intervals between them. For reduced working binary linear nondeleting MCFG presentations whose string languages satisfy \((f,h)\)-tuple substitutability, we build a typed refinement, a finite characteristic sample, and a canonical positive-data learner. Once the sample contains this characteristic sample and remains contained in the target language, the learner reconstructs the language exactly. Consequently, for fixed fan-out bound \(f\) and fixed explicit \(h\), the resulting class is identifiable in the limit from positive data. Moreover, the hypothesis associated with any given finite sample is constructible in polynomial time for fixed \(f\) and fixed \(h\), including output size. Thus sentence-interface control is the finite mechanism that lifts fixed-\(h\) distributional reconstruction from context-free grammars to bounded-fan-out linear MCFGs.