Paper Info
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.
Year
DOI
Venue
2017
10.4204/EPTCS.261.7
ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE
Field
DocType
Volume
Programming language,Computer science,Free variables and bound variables,Natural language,Natural language processing,Artificial intelligence,Artificial neural network,Latent semantic analysis,Game semantics,Learnability,Syntax,Semantics
Journal
abs/1708.02319
Issue
ISSN
Citations 
261
2075-2180
0
PageRank 
References 
Authors
0.34
28
2
Name
Order
Citations
PageRank
Dan R. Ghica134630.34
Khulood AlYahya2195.17