Libraries allowing NLP practitioners but also non-specialists to leverage state-of-the-art models have been instrumental in the democratization of this technology.
We present CodeT5, a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers.
Ranked #1 on Text-to-Code Generation on CodeXGLUE - CONCODE
To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task.
Software Engineering
This introduction aims to tell the story of how we put words into computers.
Although there are many tools for natural language processing tasks in Estonian, these tools are very loosely interoperable, and it is not easy to build practical applications on top of them.
To this end, we introduce KQA Pro, a dataset for Complex KBQA including ~120K diverse natural language questions.
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare.
In this work, we conduct a thorough empirical study of the capabilities of Transformers to utilize syntactic information in different tasks.
To overcome this barrier, we propose our tool COMEX - a framework that allows researchers and developers to create and combine multiple code-views which can be used by machine learning (ML) models for various SE tasks.
This thesis report studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework.