@inproceedings{j-etal-2026-ssn,
title = "{SSN}-{CSE}-{CODECATALYSTS} at {S}em{E}val-2026 Task 13: Integrating Transformer Semantics and {AST}-Derived Structural Features for {AI}-Generated Code Detection.",
author = "J, Bhuvana and
Mahendran, Ramanan and
S, Siddharth Chandrasekar and
J, Pragatheesh and
P, Rethanya",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.141/",
pages = "1027--1031",
ISBN = "979-8-89176-414-9",
abstract = "Pre-trained transformers often struggle with multi-lingual code classification due to sequence length constraints and difficulties in explicitly capturing deep structural complexities. To address this for SemEval Task 13, a hybrid neural architecture that fuses CodeBERT{'}s semantic embeddings is proposed. Handcrafted software engineering metrics is presented, with a Head+Tail truncation strategy to preserve crucial logic in long sequences while simultaneously extracting explicit Abstract Syntax Tree (AST) features via tree-sitter{---}including maximum depth, branching factor, and cyclomatic complexity. By integrating dense language model representations with explicit structural heuristics, this work provides a robust and scalable solution for enhanced code classification."
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<abstract>Pre-trained transformers often struggle with multi-lingual code classification due to sequence length constraints and difficulties in explicitly capturing deep structural complexities. To address this for SemEval Task 13, a hybrid neural architecture that fuses CodeBERT’s semantic embeddings is proposed. Handcrafted software engineering metrics is presented, with a Head+Tail truncation strategy to preserve crucial logic in long sequences while simultaneously extracting explicit Abstract Syntax Tree (AST) features via tree-sitter—including maximum depth, branching factor, and cyclomatic complexity. By integrating dense language model representations with explicit structural heuristics, this work provides a robust and scalable solution for enhanced code classification.</abstract>
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%0 Conference Proceedings
%T SSN-CSE-CODECATALYSTS at SemEval-2026 Task 13: Integrating Transformer Semantics and AST-Derived Structural Features for AI-Generated Code Detection.
%A J, Bhuvana
%A Mahendran, Ramanan
%A S, Siddharth Chandrasekar
%A J, Pragatheesh
%A P, Rethanya
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F j-etal-2026-ssn
%X Pre-trained transformers often struggle with multi-lingual code classification due to sequence length constraints and difficulties in explicitly capturing deep structural complexities. To address this for SemEval Task 13, a hybrid neural architecture that fuses CodeBERT’s semantic embeddings is proposed. Handcrafted software engineering metrics is presented, with a Head+Tail truncation strategy to preserve crucial logic in long sequences while simultaneously extracting explicit Abstract Syntax Tree (AST) features via tree-sitter—including maximum depth, branching factor, and cyclomatic complexity. By integrating dense language model representations with explicit structural heuristics, this work provides a robust and scalable solution for enhanced code classification.
%U https://aclanthology.org/2026.semeval-1.141/
%P 1027-1031
Markdown (Informal)
[SSN-CSE-CODECATALYSTS at SemEval-2026 Task 13: Integrating Transformer Semantics and AST-Derived Structural Features for AI-Generated Code Detection.](https://aclanthology.org/2026.semeval-1.141/) (J et al., SemEval 2026)
ACL