  This paper moves the first step towards automating the composition of Answer
Set Programming (ASP) specifications. In particular, the following
contributions are provided: (i) A dataset focused on graph-related problem
specifications, designed to develop and assess tools for ASP automatic coding;
(ii) A two-step architecture, implemented in the NL2ASP tool, for generating
ASP programs from natural language specifications. NL2ASP uses neural machine
translation to transform natural language into Controlled Natural Language
(CNL) statements. Subsequently, CNL statements are converted into ASP code
using the CNL2ASP tool. An experiment confirms the viability of the approach.
