# Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation arXiv: https://arxiv.org/abs/2208.10091 ยท HTML: https://mlsanity.com/paper/2208.10091/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 22 of its 28 references that are in the map. - First posted on arXiv 22 Aug 2022. ## Builds on (most cited first, 22 of 22) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015) - [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015) - [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021) - [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021) - [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021) - [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020) - [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016) - [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018) - [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017) - [TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation](https://mlsanity.com/paper/1810.02720/) (2018) - [PyMT5: multi-mode translation of natural language and Python code with transformers](https://mlsanity.com/paper/2010.03150/) (2020) - [Latent Predictor Networks for Code Generation](https://mlsanity.com/paper/1603.06744/) (2016) - [A Retrieve-and-Edit Framework for Predicting Structured Outputs](https://mlsanity.com/paper/1812.01194/) (2018) - [Guiding Neural Machine Translation with Retrieved Translation Pieces](https://mlsanity.com/paper/1804.02559/) (2018) - [TreeGen: A Tree-Based Transformer Architecture for Code Generation](https://mlsanity.com/paper/1911.09983/) (2019) - [Retrieval-Based Neural Code Generation](https://mlsanity.com/paper/1808.10025/) (2018) - [Incorporating External Knowledge through Pre-training for Natural Language to Code Generation](https://mlsanity.com/paper/2004.09015/) (2020) - [A Grammar-Based Structural CNN Decoder for Code Generation](https://mlsanity.com/paper/1811.06837/) (2018) - [Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning](https://mlsanity.com/paper/2105.14796/) (2021) - [Exploring Dynamic Selection of Branch Expansion Orders for Code Generation](https://mlsanity.com/paper/2106.00261/) (2021) ## Built on by (most cited first, 11 of 11) - [Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models](https://mlsanity.com/paper/2402.15938/) (2024) - [EvoCodeBench: An Evolving Code Generation Benchmark Aligned with Real-World Code Repositories](https://mlsanity.com/paper/2404.00599/) (2024) - [DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories](https://mlsanity.com/paper/2405.19856/) (2024) - [PACE: Improving Prompt with Actor-Critic Editing for Large Language Model](https://mlsanity.com/paper/2308.10088/) (2023) - [Do Large Language Models Pay Similar Attention Like Human Programmers When Generating Code?](https://mlsanity.com/paper/2306.01220/) (2023) - [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023) - [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023) - [CodePAD: Sequence-based Code Generation with Pushdown Automaton](https://mlsanity.com/paper/2211.00818/) (2022) - [InfeRE: Step-by-Step Regex Generation via Chain of Inference](https://mlsanity.com/paper/2308.04041/) (2023) - [Exploring Data-Efficient Adaptation of Large Language Models for Code Generation](https://mlsanity.com/paper/2403.00046/) (2024) - [Automatic Robotic Development through Collaborative Framework by Large Language Models](https://mlsanity.com/paper/2402.03699/) (2024) ## Abstract (arXiv) Code generation aims to generate a code snippet automatically from natural language descriptions. Generally, the mainstream code generation methods rely on a large amount of paired training data, including both the natural language description and the code. However, in some domain-specific scenarios, building such a large paired corpus for code generation is difficult because there is no directly available pairing data, and a lot of effort is required to manually write the code descriptions to construct a high-quality training dataset. Due to the limited training data, the generation model cannot be well trained and is likely to be overfitting, making the model's performance unsatisfactory for real-world use. To this end, in this paper, we propose a task augmentation method that incorporates domain knowledge into code generation models through auxiliary tasks and a Subtoken-TranX model by extending the original TranX model to support subtoken-level code generation. To verify our proposed approach, we collect a real-world code generation dataset and conduct experiments on it. Our experimental results demonstrate that the subtoken-level TranX model outperforms the original TranX model and the Transformer model on our dataset, and the exact match accuracy of Subtoken-TranX improves significantly by 12.75% with the help of our task augmentation method. The model performance on several code categories has satisfied the requirements for application in industrial systems. Our proposed approach has been adopted by Alibaba's BizCook platform. To the best of our knowledge, this is the first domain code generation system adopted in industrial development environments.