Symbolic Informalization: Fluent, Productive, Multilingual

Symbolic informalization enables a reliable conversion of formal mathematics to natural language. It has the potential to make machine-checked content human-readable without loss of precision. In a traditional proof system usage, symbolic informalization generalizes the limited mechanisms of syntactic sugar into the ordinary language of mathematics. In a setting where proofs are constructed by artificial intelligence and autoformalization, symbolic informalization can explain what precisely has been constructed. This paper outlines the project Informath, which aims to show how symbolic informalization can produce fluent text with a reasonable development effort and address multiple formal and natural languages. Informath is based on an interlingual architecture, where Dedukti works as a hub between different proof systems (Agda, Lean, Rocq) and Grammatical Framework (GF) takes care of linguistic correctness and variation in different natural languages.

Urdu Morphology, Orthography and Lexicon Extraction

Urdu is a challenging language because of, first, its Perso-Arabic script and second, its morphological system having inherent grammatical forms and vocabulary of Arabic, Persian and the native languages of South Asia. This paper describes an implementation of the Urdu language as a software API, and we deal with orthography, morphology and the extraction of the lexicon. The morphology is implemented in a toolkit called Functional Morphology (Forsberg & Ranta, 2004), which is based on the idea of dealing grammars as software libraries. Therefore this implementation could be reused in applications such as intelligent search of keywords, language training and infrastructure for syntax. We also present an implementation of a small part of Urdu syntax to demonstrate this reusability.

Embedded Controlled Languages

Inspired by embedded programming languages, an embedded CNL (controlled natural language) is a proper fragment of an entire natural language (its host language), but it has a parser that recognizes the entire host language. This makes it possible to process out-of-CNL input and give useful feedback to users, instead of just reporting syntax errors. This extended abstract explains the main concepts of embedded CNL implementation in GF (Grammatical Framework), with examples from machine translation and some other ongoing work.