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. Our model combines a regular link prediction model learned from a knowledge graph with word embeddings learned from a textual corpus. After training both independently, we learn a transformation to map the embeddings of an entity's name and description to the graph-based embedding space. In experiments on several datasets including FB20k, DBPe-dia50k and our new dataset FB15k-237-OWE, we demonstrate competitive results. Particularly, our approach exploits the full knowledge graph structure even when textual descriptions are scarce, does not require a joint training on graph and text, and can be applied to any embedding-based link prediction model, such as TransE, ComplEx and DistMult.

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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. CONTEST is large-scale (with 365k datapoints). Besides the test code and tested code, it also features context code called by either. We found context to be crucial for accurately predicting assertions. We also introduce baselines based on transformer encoderdecoders, and study the effects of including syntactic information and context. Overall, our models achieve a BLEU score of 38.2, while only generating unparsable code in 1.92% of cases.

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We address contextualized code retrieval, the search for code snippets helpful to fill gaps in a partial input program. Our approach facilitates a large-scale self-supervised contrastive training by splitting source code randomly into contexts and targets. To combat leakage between the two, we suggest a novel approach based on mutual identifier masking, dedentation, and the selection of syntax-aligned targets. Our second contribution is a new dataset for direct evaluation of contextualized code retrieval, based on a dataset of manually aligned subpassages of code clones. Our experiments demonstrate that our approach improves retrieval substantially, and yields new state-ofthe-art results for code clone and defect detection.