Papers with PL
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
| Approach: | They propose a framework for aspect-based sentiment analysis that uses annotated data to train ABSA models. |
| Outcome: | The proposed framework surpasses previous methods on benchmarks. |
Leveraging Generative AI for Extracting Business Requirements from Legacy COBOL and PL/I Code (2026.acl-industry)
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| Challenge: | Existing pipelines for extracting business requirements from legacy systems are difficult because they are scattered across interdependent programs and data definitions. |
| Approach: | They propose an LLM-augmented reverse-engineering pipeline that provides deterministic parsing and schema-constrainedLLM generation with bidirectional traceability. |
| Outcome: | The proposed pipeline achieves 93% agreement with expert-authored business rules and reduces documentation effort by approximately 70% on 3.4M lines across workloads. |
C3PO: A Lightweight Copying Mechanism for Translating Pseudocode to Code (2022.aacl-srw)
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| Challenge: | Existing low-code translators that translate pseudocode to code are expensive in terms of data and compute. |
| Approach: | They propose a lightweight alternative that exploits the property of code wherein most tokens can be simply copied from the pseudocode. |
| Outcome: | The proposed model reduces the computational cost and vocabulary sizes while reducing the computational costs and complexity. |
Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation (2023.starsem-1)
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| Challenge: | Logical formulae are essential for scholars in many fields, including linguistics and artificial intelligence. |
| Approach: | They propose to use a Quantified Boolean Formulae (QBFs) problem to find the shortest formulae as input for a "logic-to-text" generation system. |
| Outcome: | The proposed approach improves the comprehensibility and fluency of the generated texts. |
Industry Scale Semi-Supervised Learning for Natural Language Understanding (2021.naacl-industry)
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| Challenge: | Obtaining human annotation is expensive and time-consuming process. |
| Approach: | They propose a semi-supervised learning pipeline which leverages millions of unlabeled examples to improve natural language understanding tasks. |
| Outcome: | The proposed pipeline can be used to improve natural language understanding tasks. |
A Multilingual Wikified Data Set of Educational Material (L18-1)
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Iris Hendrickx, Eirini Takoulidou, Thanasis Naskos, Katia Lida Kermanidis, Vilelmini Sosoni, Hugo de Vos, Maria Stasimioti, Menno van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Maja Popovic, Markus Egg, Antal van den Bosch
| Challenge: | a crowdsourcing effort to annotate and link parallel texts has been unsuccessful . a data set of parallel texts in eleven languages is presented . |
| Approach: | They present a wikified data set of English sentences linked to Wikipedia pages . they use crowdsourcing to annotate the texts and perform crowdsourcing for complex annotations . |
| Outcome: | The proposed data set is valuable as it constitutes a rich resource . it includes annotated data of English sentences linked to translations in eleven languages . |
CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)
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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou
| Challenge: | Large pre-trained models have improved performance on a variety of natural language processing tasks. |
| Approach: | They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators. |
| Outcome: | The proposed model performs better on two NL-PL applications by fine-tuning model parameters. |
Code4Struct: Code Generation for Few-Shot Event Structure Prediction (2023.acl-long)
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| Challenge: | Large Language Models (LLMs) trained on a mixture of text and code have demonstrated impressive capability in translating natural language (NL) into structured code. |
| Approach: | They propose to use programming language (PL) inheritance and type annotations to translate text into code to tackle structured prediction tasks. |
| Outcome: | The proposed model outperforms existing models on 20-shot data by 29.5% absolute F1. |
Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
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| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
MojoBench: Language Modeling and Benchmarks for Mojo (2025.findings-naacl)
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| Challenge: | Mojo is a programming language that has been praised for its speed and performance over Python. |
| Approach: | They propose a framework for Mojo code generation that evaluates code Large Language Models (LLMs) they propose 'mojo-Coder' which is the first LLM pretrained and fine-tuned for MoJO code generation . |
| Outcome: | MojoBench is the first framework for mojo code generation . it achieves a 30-35% performance improvement over leading models like GPT-4o and Claude-3.5-Sonnet . |
A Regex Minimization Benchmark: A PSPACE-Complete Challenge for Language Models (2026.eacl-long)
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| Challenge: | Language models (LMs) have demonstrated impressive reasoning capabilities across domains . but their ability to handle PSPACE-complete problems remains underexplored . a new benchmark for regex minimization is proposed to evaluate LMs' reasoning capabilities . |
| Approach: | They propose a benchmark for regex minimization to evaluate LMs' reasoning power . they use a million regexes paired with their minimal equivalents to evaluate their performance . |
| Outcome: | The proposed model can solve NP-complete problems, but their ability to handle PSPACE-complete ones remains underexplored. |
DIP: Dead code Insertion based Black-box Attack for Programming Language Model (2023.acl-long)
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| Challenge: | Existing methods to attack natural language models are difficult to apply due to the requirements. |
| Approach: | They propose a black-box attack method that generates adversarial examples using dead code insertion. |
| Outcome: | The proposed method outperforms the state-of-the-art black-box attack in both attack efficiency and attack quality on 9 victim downstream-task large code models. |
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (2022.coling-1)
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Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang
| Challenge: | Existing methods to parse natural language into structured logical expressions have limitations due to paucity of labeled data. |
| Approach: | They propose a scoring model to automatically learn a model-based reward . they also propose introducing a Chinese-PL/FOL dataset to compensate for paucity of labeled data . |
| Outcome: | The proposed model outperforms competitors on several datasets. |
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation (2025.coling-main)
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| Challenge: | Using large language models to generate code has shown significant promise, but selecting effective examples to improve generation quality remains a challenging task. |
| Approach: | They propose a framework that utilizes algorithmic plans to identify and retrieve effective examples. |
| Outcome: | The proposed framework outperforms the state-of-the-art RAG methods in code generation even when the source and target languages match or differ. |
Bridge-Coder: Transferring Model Capabilities from High-Resource to Low-Resource Programming Language (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel at generating code for high-resource programming languages (HRPLs) however, they struggle significantly with low-resourced programming languages such as D, exacerbating the digital divide. |
| Approach: | They propose a method to generate LRPL data using LLM's general knowledge, HRPL proficiency, and in-context learning capabilities. |
| Outcome: | The proposed method improves on R, D, Racket, and Bash, while maintaining the same quality. |
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages (2023.findings-acl)
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| Challenge: | ERNIE-Code is a unified pre-trained language model for 116 NLs and 6 PLs. |
| Approach: | They propose a unified pre-trained language model for 116 NLs and 6 PLs . they employ span-corruption language modeling that learns patterns from monolingual NL or PL . |
| Outcome: | The proposed model outperforms previous multilingual models for NL or NL across end tasks. |
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)
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| Challenge: | Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages. |
| Approach: | They propose a unified pre-trained encoder-decoder Transformer model that leverages the code semantics conveyed from the developer-assigned identifiers. |
| Outcome: | The proposed model outperforms existing models on understanding and generation tasks and can capture semantic information from code. |
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
CodeComplex: Dataset for Worst-Case Time Complexity Prediction (2025.findings-emnlp)
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| Challenge: | Reasoning ability of large language models (LLMs) is crucial in complex decision-making tasks. |
| Approach: | They propose to use code time complexity prediction to assess LLMs' reasoning ability. |
| Outcome: | The proposed dataset comprises 4,900 Java codes and an equivalent number of Python codes. |
Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL (2026.findings-acl)
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Zhaofeng Wu, Shiqi Wang, Boya Peng, Anuj Kumar Goyal, Melanie Kambadur, Sebastian Ruder, Yoon Kim, Chloe Bi
| Challenge: | Modern language models demonstrate impressive coding capabilities in common programming languages (PLs) but their performance in lower-resource PLs is often limited by training data availability. |
| Approach: | They propose a zero-shot cross-programming-language transfer task for code RL . they propose RL training in a source PL fails to improve performance on other target PLs . |
| Outcome: | The proposed approach improves transferability in Llama-3.1 code generation on parallel-stack model . it also improves performance on other target PLs, compared to single-PL SFT . |
How Programming Concepts and Neurons Are Shared in Code Language Models (2025.findings-acl)
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| Challenge: | Several studies have focused on programming languages in a monolingual setting, but most focus on programming language models. |
| Approach: | They perform a few-shot translation task on 21 PL pairs using two Llama-based models and decode the embeddings of intermediate layers. |
| Outcome: | The proposed model assigns high probability to English tokens in the second half of the intermediate layers and language-specific neurons are concentrated in the bottom layers . the model's concept space is closer to English (including PL keywords) and the model is more efficient at identifying language-related neurons. |