no code implementations • ACL (NLP4Prog) 2021 • Johannes Villmow, Jonas Depoix, Adrian Ulges
We introduce CONTEST, a benchmark for NLP-based unit test completion, the task of predicting a test’s assert statements given its setup and focal method, i. e. the method to be tested.
1 code implementation • COLING (TextGraphs) 2020 • Haseeb Shah, Johannes Villmow, Adrian Ulges
We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training.
no code implementations • COLING 2022 • Johannes Villmow, Viola Campos, Adrian Ulges, Ulrich Schwanecke
We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program.
1 code implementation • 19 Jun 2019 • Haseeb Shah, Johannes Villmow, Adrian Ulges, Ulrich Schwanecke, Faisal Shafait
We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i. e. to predict facts for entities unseen in training based on their textual description.