Year: 2017
Type: conference-paper
Abstract: We consider the problem of translating high-level textual descriptions to formal representations in technical documentation as part of an effort to model the meaning of such documentation.We focus specifically on the problem of learning translational correspondences between text descriptions and grounded representations in the target documentation, such as formal representation of functions or code templates.Our approach exploits the parallel nature of such documentation, or the tight coupling between high-level text and the low-level representations we aim to learn.Data is collected by mining technical documents for such parallel text-representation pairs, which we use to train a simple semantic parsing model.We report new baseline results on sixteen novel datasets, including the standard library documentation for nine popular programming languages across seven natural languages, and a small collection of Unix utility manuals.1. Java Documentation * Returns the greater of two long values * * @param a an argument * @param b another argument * @return the larger of a and b * @see java.lang.Long#MAX VALUE * / public static long max(long a, long b) 2. Clojure Documentation (defn random-sample "Returns items from coll with random probability of prob (0.0 -1.0)" ([prob coll] ...)) 3. PHP documentation (French) Ajoute une valeur comme dernier élément * * @param value La valeur á ajouter * @see ArrayIterations::next() * / public void append(mixed $value)
DOI: 10.18653/v1/p17-1148
Authors: Kyle Richardson, Jonas Kuhn
Institution: University of Stuttgart
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Deutsche Forschungsgemeinschaft
Award: G3344510221
OpenAlex ID: W2615909541