@inproceedings{ca7a7074fd0149b3bb916f912bded4f7,
title = "On the Learnability of Programming Language Semantics",
abstract = "Game semantics is a powerful method of semantic analysis for programming languages. It gives mathematically accurate models (“fully abstract”) for a wide variety of programming languages. Game semantic models are combinatorial characterisations of all possible interactions between a term and its syntactic context. Because such interactions can be concretely represented as sets of sequences, it is possible to ask whether they can be learned from examples. Concretely, we are using long short-term memory neural nets (LSTM), a technique which proved effective in learning natural languages for automatic translation and text synthesis, to learn game-semantic models of sequential and concurrent versions of Idealised Algol (IA), which are algorithmically complex yet can be concisely described. We will measure how accurate the learned models are as a function of the degree of the term and the number of free variables involved. Finally, we will show how to use the learned model to perform latent semantic analysis between concurrent and sequential Idealised Algol.",
author = "Ghica, {Dan R.} and Khulood Alyahya",
year = "2017",
month = nov,
day = "29",
doi = "10.4204/EPTCS.261.7",
language = "English",
series = "Electronic Proceedings in Theoretical Computer Science",
publisher = "Open Publishing Association",
pages = "57--75",
editor = "Massimo Bartoletti and Laura Bocchi and Ludovic Henrio and Sophia Knight",
booktitle = "Proceedings 10th Interaction and Concurrency Experience (ICE 2017)",
note = "10th Interaction and Concurrency Experience (ICE 2017) ; Conference date: 22-06-2017 Through 22-06-2017",
}