Towards Synthesizing Complex Programs from Input-Output Examples

Understand

In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples.

• Albeit its significant progress, the programs that can be synthesized by state-of-the-art approaches are still simple in terms of their complexity.
• In this work, we move a significant step forward along this direction by proposing a new class of challenging tasks in the domain of program synthesis from input-output examples: learning a context-free parser from pairs of input programs and their parse trees.
• We show that this class of tasks are much more challenging than previously studied tasks, and the test accuracy of existing approaches is almost 0%.