70 papers with code • 9 benchmarks • 17 datasets
Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity.
Image source: Measuring Coding Challenge Competence With APPS
Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications.
In order to formalize the behaviors of the HP in distributed DL and quantitatively evaluate the cost caused by HP, we are studying Bridging DL composed by a double-level execution model associated with a symbolic cost model.
The dataset is comprehensive in that it spans problems of a range of difficulties and domains, ranging from trivial string manipulation problems, to classic programming puzzles (e. g., Tower of Hanoi), to interview/competitive-programming problems (e. g., dynamic programming), to longstanding open problems in algorithms and mathematics (e. g., factoring).
Deep learning models typically use single-precision (FP32) floating point data types for representing activations and weights, but a slew of recent research work has shown that computations with reduced-precision data types (FP16, 16-bit integers, 8-bit integers or even 4- or 2-bit integers) are enough to achieve same accuracy as FP32 and are much more efficient.
We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs).
Ranked #2 on Semantic Parsing on ATIS
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.