Semantic Code Classification for Automated Machine Learning
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Abstract (arXiv)
A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we present a semantic code classification task and discuss methods for solving this problem on the Natural Language to Machine Learning (NL2ML) dataset.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 25 Jan 2022.
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References · 9
- 2021Evaluating Large Language Models Trained on Codecited by 8,053
- 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
- 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
- 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
- 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
- 2019BPE-Dropout: Simple and Effective Subword Regularizationcited by 189
- 2020PyMT5: multi-mode translation of natural language and Python code with transformerscited by 80
- 2021In-IDE Code Generation from Natural Language: Promise and Challengescited by 41
- 2014Naive Bayes and Text Classification I - Introduction and Theorycited by 5
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