Benchmarking Language Models for Code Syntax Understanding
Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen, Dawn Song
Abstract
Pre-trained language models have demonstrated impressive performance in both natural language processing and program understanding, which represent the input as a token sequence without explicitly modeling its structure. Some prior works show that pre-trained language models can capture the syntactic rules of natural languages without finetuning on syntax understanding tasks. However, there is limited understanding of how well pre-trained models understand the code structure so far. In this work, we perform the first thorough benchmarking of the state-of-the-art pre-trained models for identifying the syntactic structures of programs. Specifically, we introduce CodeSyntax, a large-scale dataset of programs annotated with the syntactic relationships in their corresponding abstract syntax trees. Our key observation is that pre-training on massive code data does not result in decent code syntax understanding. In fact, these pre-trained programming language models fail to match the performance of naive baselines based on positional offsets and keywords. We also present a natural language benchmark to highlight the differences between natural languages and programming languages in terms of understanding corresponding syntactic structures. Our findings point out key limitations of existing pre-training methods and suggest the importance of modeling syntactic structures for the programming language.- Anthology ID:
- 2022.findings-emnlp.224
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3071–3093
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.224
- DOI:
- 10.18653/v1/2022.findings-emnlp.224
- Cite (ACL):
- Da Shen, Xinyun Chen, Chenguang Wang, Koushik Sen, and Dawn Song. 2022. Benchmarking Language Models for Code Syntax Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3071–3093, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Benchmarking Language Models for Code Syntax Understanding (Shen et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.findings-emnlp.224.pdf