2017 IJCNLP IJCNLP 2017

Dialog for Language to Code

Abstract

AbstractGenerating computer code from natural language descriptions has been a long-standing problem. Prior work in this domain has restricted itself to generating code in one shot from a single description. To overcome this limitation, we propose a system that can engage users in a dialog to clarify their intent until it has all the information to produce correct code. To evaluate the efficacy of dialog in code generation, we focus on synthesizing conditional statements in the form of IFTTT recipes.

🌉 Interdisciplinary Bridge — Computer Science and Natural Language Processing
🧭 Keyword Pioneer — intent clarification
🐣 Hot Topic Early Bird — code generation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio