Papers with AST
Revisiting Code Similarity Evaluation with Abstract Syntax Tree Edit Distance (2024.acl-short)
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| Challenge: | Abstract Syntax Tree (AST) editing distance is a new evaluation metric for code generation tasks. |
| Approach: | They propose, optimize, and publish an enhanced version of Tree Similarity of Edit Distance (TSED) based on AST editing distance and prompt-based GPT similarity scores. |
| Outcome: | The proposed metric is an enhanced version of Tree Similarity of Edit Distance (TSED) it is compared to BLEU score, execution match, and Jaccard similarity across languages. |
GAP-Gen: Guided Automatic Python Code Generation (2023.eacl-srw)
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| Challenge: | Several previous approaches convert a sentence into a formal statement by mapping verbs to functions in the formal language. |
| Approach: | They propose a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. |
| Outcome: | The proposed method achieves better results on automatic Python code generation task than previous methods. |
G3R: A Graph-Guided Generate-and-Rerank Framework for Complex and Cross-domain Text-to-SQL Generation (2023.findings-acl)
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| Challenge: | Existing approaches to complex and cross-domain Text-to-SQL generation lack domain knowledge . domain knowledge is not incorporated to enhance their ability to generalise to unseen databases. |
| Approach: | They propose a framework called G3R for complex and cross-domain Text-to-SQL generation . they propose re-ranking SQL queries based on domain knowledge and a graph-guided SQL generator . |
| Outcome: | The proposed framework achieves state-of-the-art results on the Spider and Spider-DK benchmarks. |
Pass-Tuning: Towards Structure-Aware Parameter-Efficient Tuning for Code Representation Learning (2023.findings-emnlp)
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| Challenge: | Code pre-trained models have been proposed and widely applied in the domain of code intelligence. |
| Approach: | They propose a method that uses a plug-and-play graph neural network module as a tunable prefix to exploit structural information of source code. |
| Outcome: | The proposed method exploits structural information of source code and could replace full fine-tuning. |
Automatic Song Translation for Tonal Languages (2022.findings-acl)
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| Challenge: | Existing automatic song translation systems for tonal languages do not match the number of notes and beat the original rhythm of the song. |
| Approach: | They propose three criteria for effective AST: preserving meaning, singability and intelligibility. |
| Outcome: | The proposed system balances semantics and singability with human evaluations. |
Pisets: A Robust Speech Recognition System for Lectures and Interviews (2025.naacl-industry)
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Ivan Bondarenko, Daniil Grebenkin, Oleg Sedukhin, Mikhail Klementev, Derunets Roman, Lyudmila Budneva
| Challenge: | Sustainable speech recognition systems are essential for scientists, journalists, and anyone processing audio recordings of interviews and meetings. |
| Approach: | They propose a speech-to-text system "Pisets" which is based on a three-component architecture aimed at improving speech recognition accuracy while minimizing errors and hallucinations associated with the Whisper model. |
| Outcome: | The proposed system ensures robust transcribing of long audio data across various acoustic conditions compared to WhisperX and the usual Whisper model. |
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)
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| Challenge: | Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors. |
| Approach: | They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training. |
| Outcome: | The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets. |
AfriVox: Probing Multilingual and Accent Robustness of Speech LLMs (2026.eacl-long)
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Busayo Awobade, Mardhiyah Sanni, Tassallah Abdullahi, Chibuzor Okocha, Kelechi Ezema, Devendra Deepak Kayande, Lukman Enegi Ismaila, Tobi Olatunji, Gloria Ashiya Katuka
| Challenge: | Recent advances in multimodal and speech-native large language models have delivered impressive speech recognition, translation, understanding, and question-answering capabilities for high-resource languages. |
| Approach: | They propose to benchmark African languages and African-accented French, Arabic, and 100+ African English accents across 20 African languages. |
| Outcome: | The proposed model outperforms traditional speech transcription and translation models in African languages and non-native French or English accents. |
Learning Sequential and Structural Information for Source Code Summarization (2021.findings-acl)
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| Challenge: | Existing models that learn both sequential and structural features of source code are limited in their use. |
| Approach: | They propose a model that learns both the sequential and the structural features of code for source code summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art for source code summarization by experiments and human evaluations. |
ART: Attention-Regularized Transformers for Multi-Modal Robustness (2026.findings-eacl)
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| Challenge: | Existing approaches to enhancing robustness are domain-specific or lack formal guarantees. |
| Approach: | They propose a framework that enhances robustness across modalities by regularizing attention maps under adversarial perturbations. |
| Outcome: | The proposed framework improves robustness across modalities and training on IMDB, QNLI, CIFAR-10, Cifar-100, and Imagenette. |
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure (2022.findings-emnlp)
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| Challenge: | Existing code pre-trained models fail to consider inherent characteristics of codes . Existing methods to interpret code pretrained model fail to take into account inherent characteristics . |
| Approach: | They propose a probing method to quantitatively interpret how CodePTMs attend code structure. |
| Outcome: | The proposed method denoises input code sequences and measures commonality between token-level attention scores and pair-wise distances between corresponding AST nodes. |
NL2Logic: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models (2026.findings-eacl)
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| Challenge: | Structured reasoning approaches that parse first-order logic rules from natural language lack syntax control and semantic faithfulness. |
| Approach: | They propose a structured reasoning paradigm that parses first-order logic rules from natural language and delegates inference to automated solvers. |
| Outcome: | a proposed framework parses first-order logic rules from natural language and delegates inference to automated solvers. |
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees (2021.emnlp-main)
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| Challenge: | Existing methods for code summarization do not capture rich information in ASTs . existing methods are labor-intensive and time-consuming to document code with good summaries manually. |
| Approach: | They propose a model that hierarchically splits and reconstructs ASTs by a neural network . they propose to use AST embeddings and a vanilla code token encoder to generate the model . |
| Outcome: | The proposed model splits and reconstructs ASTs into subtrees and then aggregates embeddings of subtreas to get the complete AST. |
Analysis of Tree-Structured Architectures for Code Generation (2021.findings-acl)
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| Challenge: | Code generation is the task of generating code snippets from input user specifications written in natural language (NL). |
| Approach: | They evaluate the significance of input parse trees for code generation by using constituency-based parsers as input and an abstract syntax tree as the target. |
| Outcome: | The proposed models on a Python-based code generation dataset and a semantic parsing dataset show that constituency trees encoded using a structure-aware model improve performance. |
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation (2021.acl-long)
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| Challenge: | Existing code generation models model abstract syntax tree (AST) but not suitable for all multi-branch nodes. |
| Approach: | They propose to equip a Seq2Tree model with a branch selector to determine optimal expansion orders for multi-branch nodes. |
| Outcome: | The proposed model can determine optimal expansion orders of branches for multi-branch nodes. |
UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)
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| Challenge: | Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks . |
| Approach: | They propose a unified cross-modal pre-trained model for programming language that leverages cross-module contents like AST and code comment to enhance code representation. |
| Outcome: | The proposed model achieves state-of-the-art on most code-related tasks and compares with existing models on zero-shot code-to-code search. |
Boosting Code Summarization by Embedding Code Structures (2022.coling-1)
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| Challenge: | Recent work on code summarization relies on structural information from the abstract syntax tree (AST) of source codes. |
| Approach: | They propose a program dependency graph (PDG) that represents the structure of a code more effectively. |
| Outcome: | The proposed model improves the performance of an out-of-domain benchmark dataset and the measure SBERT score. |
Robust Singing Voice Transcription Serves Synthesis (2024.acl-long)
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| Challenge: | Current AST methods struggle with accuracy and robustness when used for practical annotation. |
| Approach: | They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets. |
| Outcome: | The proposed model outperforms baseline models on enlarged, automatically annotated datasets. |
CODESTRUCT: Code Agents over Structured Action Spaces (2026.acl-long)
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| Challenge: | LLM-based code agents treat repositories as unstructured text, fail to produce valid patches . authors propose a structure-aware interface that exposes a codebase as a programmable action space . |
| Approach: | They propose to reframe the codebase as a structured action space where agents operate on named AST entities rather than text spans. |
| Outcome: | Evaluated on six LLMs, the proposed framework improves Pass@1 accuracy by 1.2-5.0% and reduces token consumption by 12-38%. |
BLOCSUM: Block Scope-based Source Code Summarization via Shared Block Representation (2023.findings-acl)
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| Challenge: | Abstract Syntax Tree (AST) and sequence of code tokens are useful for code summarization. |
| Approach: | They propose a shared block position embedding to represent various code blocks . they also develop variant ASTs to learn rich information such as block and global dependencies . |
| Outcome: | The proposed method improves on two real-world datasets, including ablation studies and a human evaluation. |
MASSIVE-Agents: A Benchmark for Multilingual Function-Calling in 52 Languages (2025.findings-emnlp)
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| Challenge: | Using the original dataset, we cleaned up the MASSIVE dataset and reformatted it for evaluation within the Berkeley Function-Calling Leaderboard framework. |
| Approach: | They present a new benchmark for assessing multilingual function calling across 52 languages . they clean the original MASSIVE dataset and reformat it for evaluation . |
| Outcome: | The new benchmark covers 55 functions and 286 arguments in 52 languages. |
VerilogLAVD: LLM-Aided Pattern Generation for Verilog CWE Detection (2026.acl-long)
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| Challenge: | Existing static analysis tools focus on functional correctness and depend heavily on manual rules. |
| Approach: | They propose a framework that generates executable Traversal Detection Patterns (TDPs) to help detect hardware vulnerabilities. |
| Outcome: | The proposed framework improves the F1 score by 133% compared to LLM-based methods. |
InteracSPARQL : An Interactive System for SPARQL Query Refinement Using Natural Language Explanations (2026.findings-acl)
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| Challenge: | Existing approaches for SPARQL generation rely on one-turn models. |
| Approach: | They propose a training-free interactive refinement pipeline that acts as a plug-and-play enhancement for existing SPARQL systems. |
| Outcome: | The proposed approach improves the accuracy of base models without fine-tuning . it transforms potentially flawed queries from any source into verifiable code . |
Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing (2026.findings-acl)
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| Challenge: | Large language models have been used in reasoning-heavy planning tasks but their tendency to hallucinate unsafe outputs poses risks. |
| Approach: | They propose a method for searching the space of prompt perturbations using adaptive stress testing with Monte-Carlo tree search. |
| Outcome: | The proposed method can detect scenarios, sensor configurations, and prompt phrasing that cause models to act with high uncertainty or crash. |