GF + MMT = GLF -- From Language to Semantics through LF

TL;DR

The paper introduces the Grammatical Logical Framework (GLF), combining GF and MMT, to facilitate the development of natural language understanding systems that integrate syntax and semantics within compatible logical frameworks.

Contribution

It presents the novel GLF framework that enables parallel development of language parsing and logical semantics using compatible tools GF and MMT.

Findings

GLF allows integrated development of syntax and semantics.

It supports multiple logics for meaning representation.

The framework facilitates experimentation with NLU pipelines.

Abstract

These days, vast amounts of knowledge are available online, most of it in written form. Search engines help us access this knowledge, but aggregating, relating and reasoning with it is still a predominantly human effort. One of the key challenges for automated reasoning based on natural-language texts is the need to extract meaning (semantics) from texts. Natural language understanding (NLU) systems describe the conversion from a set of natural language utterances to terms in a particular logic. Tools for the co-development of grammar and target logic are currently largely missing. We will describe the Grammatical Logical Framework (GLF), a combination of two existing frameworks, in which large parts of a symbolic, rule-based NLU system can be developed and implemented: the Grammatical Framework (GF) and MMT. GF is a tool for syntactic analysis, generation, and translation with complex natural language grammars and MMT can be used to specify logical systems and to represent knowledge in them. Combining these tools is possible, because they are based on compatible logical frameworks: Martin-L\"of type theory and LF. The flexibility of logical frameworks is needed, as NLU research has not settled on a particular target logic for meaning representation. Instead, new logics are developed all the time to handle various language phenomena. GLF allows users to develop the logic and the language parsing components in parallel, and to connect them for experimentation with the entire pipeline.