Textual Features for Programming by Example
This work addresses the challenge of making Programming by Example more scalable for text processing tasks, though it is incremental as it builds on existing methods with a learning-based approach.
The paper tackles the problem of inefficient brute-force search in Programming by Example for text processing by using textual features from examples to guide program composition and ranking, resulting in faster search and efficient inference on various tasks.
In Programming by Example, a system attempts to infer a program from input and output examples, generally by searching for a composition of certain base functions. Performing a naive brute force search is infeasible for even mildly involved tasks. We note that the examples themselves often present clues as to which functions to compose, and how to rank the resulting programs. In text processing, which is our domain of interest, clues arise from simple textual features: for example, if parts of the input and output strings are permutations of one another, this suggests that sorting may be useful. We describe a system that learns the reliability of such clues, allowing for faster search and a principled ranking over programs. Experiments on a prototype of this system show that this learning scheme facilitates efficient inference on a range of text processing tasks.