# Automatic Inference of Minimalist Grammars using an SMT-Solver

## TL;DR

This paper presents a new method for automatically inferring Minimalist Grammars by encoding a parser as an SMT-solver system and using annotated sentences to derive grammars that align with modern syntactic theories.

## Contribution

It introduces a novel SMT-based parser for Minimalist Grammars and a procedure for inferring grammars from annotated sentences, aligning with current syntactic theories.

## Key findings

- Inferred grammars match syntactic annotations accurately.
- Optimal grammars align with contemporary syntactic theories.
- Method demonstrates effective grammar inference from annotated data.

## Abstract

We introduce (1) a novel parser for Minimalist Grammars (MG), encoded as a system of first-order logic formulae that may be evaluated using an SMT-solver, and (2) a novel procedure for inferring Minimalist Grammars using this parser. The input to this procedure is a sequence of sentences that have been annotated with syntactic relations such as semantic role labels (connecting arguments to predicates) and subject-verb agreement. The output of this procedure is a set of minimalist grammars, each of which is able to parse the sentences in the input sequence such that the parse for a sentence has the same syntactic relations as those specified in the annotation for that sentence. We applied this procedure to a set of sentences annotated with syntactic relations and evaluated the inferred grammars using cost functions inspired by the Minimum Description Length principle and the Subset principle. Inferred grammars that were optimal with respect to certain combinations of these cost functions were found to align with contemporary theories of syntax.