# A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

arXiv: https://arxiv.org/abs/2010.12663 · HTML: https://mlsanity.com/paper/2010.12663/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation](https://mlsanity.com/paper/2305.00909/) (2023)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [NEO-BENCH: Evaluating Robustness of Large Language Models with Neologisms](https://mlsanity.com/paper/2402.12261/) (2024)
- [CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code](https://mlsanity.com/paper/2308.00683/) (2023)
- [Improving Code Autocompletion with Transfer Learning](https://mlsanity.com/paper/2105.05991/) (2021)
- [Token-free Models for Sarcasm Detection](https://mlsanity.com/paper/2505.01006/) (2025)

## Abstract (arXiv)

There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers. Our method can be treated as a preprocessing step and, therefore, allows for easy implementation. We show that the proposed OOV anonymization method significantly improves the performance of the Transformer in two code processing tasks: code completion and bug fixing.