Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback

Gemmell, C., Dalton, J. and Rossetto, F. (2020) Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback. In: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), Xi'an, China, 25-30 Jul 2020, pp. 2005-2008. ISBN 9781450380164 (doi:10.1145/3397271.3401215)

Full text not currently available from Enlighten.


Tools capable of automatic code generation have the potential to augment programmer's capabilities. While straightforward code retrieval is incorporated into many IDEs, an emerging area is explicit code generation. Code generation is currently approached as a Machine Translation task, with Recurrent Neural Network (RNN) based encoder-decoder architectures trained on code-description pairs. In this work we introduce and study modern Transformer architectures for this task. We further propose a new model called the Relevance Transformer that incorporates external knowledge using pseudo-relevance feedback. The Relevance Transformer biases the decoding process to be similar to existing retrieved code while enforcing diversity. We perform experiments on multiple standard benchmark datasets for code generation including Django, Hearthstone, and CoNaLa. The results show improvements over state-of-the-art methods based on BLEU evaluation. The Relevance Transformer model shows the potential of Transformer-based architectures for code generation and introduces a method of incorporating pseudo-relevance feedback during inference.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Gemmell, Mr Carlos and Rossetto, Mr Federico and Dalton, Dr Jeff
Authors: Gemmell, C., Dalton, J., and Rossetto, F.
College/School:College of Science and Engineering > School of Computing Science
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record