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Title: ADELT: Transpilation between Deep Learning Frameworks
We propose the Adversarial DEep Learning Transpiler (ADELT), a novel approach to source-to-source transpilation between deep learning frameworks. ADELT uniquely decouples code skeleton transpilation and API keyword mapping. For code skeleton transpilation, it uses few-shot prompting on large language models (LLMs), while for API keyword mapping, it uses contextual embeddings from a code-specific BERT. These embeddings are trained in a domain-adversarial setup to generate a keyword translation dictionary. ADELT is trained on an unlabeled web-crawled deep learning corpus, without relying on any hand-crafted rules or parallel data. It outperforms state-of-the-art transpilers, improving pass@1 rate by 16.2 pts and 15.0 pts for PyTorch-Keras and PyTorch-MXNet transpilation pairs respectively. We provide open access to our code at https://github.com/gonglinyuan/adelt  more » « less
Award ID(s):
2027575 1955488
PAR ID:
10613413
Author(s) / Creator(s):
; ;
Publisher / Repository:
International Joint Conferences on Artificial Intelligence Organization
Date Published:
ISBN:
978-1-956792-04-1
Page Range / eLocation ID:
6279 to 6287
Format(s):
Medium: X
Location:
Jeju, South Korea
Sponsoring Org:
National Science Foundation
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