Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic
  Inspired by the recent evolution of deep neural networks (DNNs) in machine
learning, we explore their application to PL-related topics. This paper is the
first step towards this goal; we propose a proof-synthesis method for the
negation-free propositional logic in which we use a DNN to obtain a guide of
proof search. The idea is to view the proof-synthesis problem as a translation
from a proposition to its proof. We train seq2seq, which is a popular network
in neural machine translation, so that it generates a proof encoded as a
$\lambda$-term of a given proposition. We implement the whole framework and
empirically observe that a generated proof term is close to a correct proof in
terms of the tree edit distance of AST. This observation justifies using the
output from a trained seq2seq model as a guide for proof search.
