mlsanityData to 14 Oct 2026

A Multi-Perspective Architecture for Semantic Code Search

arXiv 2005.06980 ↗First posted 6 May 2020cs.SE cs.CL cs.LGcited by 20

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Abstract (arXiv)

The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 May 2020.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 7; 2024: 1; 2025: 1.

20 papers.

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  1. 2021CoSQA: 20,000+ Web Queries for Code Search and Question Answeringcited by 75
  2. 2023Distilled GPT for Source Code Summarizationcited by 31
  3. 2022On the Importance of Building High-quality Training Datasets for Neural Code Searchcited by 30
  4. 2021Project-Level Encoding for Neural Source Code Summarization of Subroutinescited by 24
  5. 2022CoCoSoDa: Effective Contrastive Learning for Code Searchcited by 22
  6. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  7. 2024Analyzing the Performance of Large Language Models on Code Summarizationcited by 19
  8. 2021GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Searchcited by 15
  9. 2022Code Search based on Context-aware Code Translationcited by 14
  10. 2022Accelerating Code Search with Deep Hashing and Code Classificationcited by 10
  11. 2023Semantic Similarity Loss for Neural Source Code Summarizationcited by 5
  12. 2023Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?cited by 5
8 more
  1. 2025SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrievalcited by 5
  2. 2021deGraphCS: Embedding Variable-based Flow Graph for Neural Code Searchcited by 3
  3. 2022NS3: Neuro-Symbolic Semantic Code Searchcited by 3
  4. 2023Revisiting File Context for Source Code Summarizationcited by 3
  5. 2023Statement-based Memory for Neural Source Code Summarizationcited by 2
  6. 2021Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Searchcited by 1
  7. 2023An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Codecited by 1
  8. 2022CSSAM:Code Search via Attention Matching of Code Semantics and Structurescited by 0