Teaching the Art of Computer Programming at a Distance by Generating Dialogues using Deep Neural Networks

While teaching the art of Computer Programming, students with visual impairments (VI) are disadvantaged, because speech is their preferred modality. Existing accessibility assistants can only read out predefined texts sequentially, word-for-word, sentence-for-sentence, whilst the presentations of programming concepts could be conveyed in a more structured way. Earlier we have shown that deep neural networks such as Tree-Based Convolutional Neural Networks (TBCNN) and Gated Graph Neural Networks (GGNN) can be used to classify algorithms across different programming languages with over 90% accuracy. Furthermore, TBCNN or GGNN have been shown useful for generating natural and conversational dialogues from natural language texts. In this paper, we propose a novel pedagogy called “Programming Assistant”, by creating a personal tutor that can respond to voice commands, which trigger an explanation of programming concepts, hands-free. We generate dialogues using DNNs, which substitute code with the names of algorithms characterising the programs, and we read aloud descriptions of the code. Furthermore, the application of the dialogue generation can be embodied into an Alexa Skill, which turns them into fully natural voices, forming the basis of a smart assistant to handle a large number of formative questions in teaching the Art of Computer Programming at a distance

VirtualHome: Simulating Household Activities via Programs

In this paper, we are interested in modeling complex activities that occur in a typical household. We propose to use programs, i.e., sequences of atomic actions and interactions, as a high level representation of complex tasks. Programs are interesting because they provide a non-ambiguous representation of a task, and allow agents to execute them. However, nowadays, there is no database providing this type of information. Towards this goal, we first crowd-source programs for a variety of activities that happen in people's homes, via a game-like interface used for teaching kids how to code. Using the collected dataset, we show how we can learn to extract programs directly from natural language descriptions or from videos. We then implement the most common atomic (inter)actions in the Unity3D game engine, and use our programs to "drive" an artificial agent to execute tasks in a simulated household environment. Our VirtualHome simulator allows us to create a large activity video dataset with rich ground-truth, enabling training and testing of video understanding models. We further showcase examples of our agent performing tasks in our VirtualHome based on language descriptions.Comment: CVPR 2018 (Oral

Learning Semantic Correspondences in Technical Documentation

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.Comment: accepted to ACL-201