Pretrained transformer-based models have shown high performance in natural language generation task. However, a new wave of interest has surged: automatic programming language generation. This task consists of translating natural language instructions to a programming code. Despite the fact that well-known pretrained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformers neural network. It aims to generate java source code from natural language text. JaCoText leverages advantages of both natural language and code generation models. More specifically, we study some findings from the state of the art and use them to (1) initialize our model from powerful pretrained models, (2) explore additional pretraining on our java dataset, (3) carry out experiments combining the unimodal and bimodal data in the training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.

Amazon is testing a variety of robotic and smart technology solutions designed to create a safer workplace. At its Amazon Robotics and Advanced Technology labs located near Seattle, in Boston, and in Northern Italy, the e-tail giant is working on new technologies to help move totes, carts, and packages through its facilities. In the Seattle-area research and innovation lab, one project in early development involves the use of motion-capture technology to assess the movement of volunteer employees in a lab setting. These employees perform tasks that are common in many Amazon facilities, such as the movement of totes, which carry products through robotic fulfillment centers. Motion-capture software enables Amazon scientists and researchers to more accurately compare data captured in a lab environment to industry standards, rather than other traditional ergonomic modeling tools.