ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929287884439552 |
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| author | Li, Zehan Zhang, Jianfei Yin, Chuantao Ouyang, Yuanxin Rong, Wenge |
| author_facet | Li, Zehan Zhang, Jianfei Yin, Chuantao Ouyang, Yuanxin Rong, Wenge |
| contents | Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16702 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search Li, Zehan Zhang, Jianfei Yin, Chuantao Ouyang, Yuanxin Rong, Wenge Computation and Language Information Retrieval Software Engineering Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally structured mixed-modal QA pairs. To validate its effectiveness, we propose a modality-agnostic contrastive pre-training approach to improve the alignment of text and code representations of current code language models. Compared to previous models that primarily employ bimodal and unimodal pairs extracted from CodeSearchNet for pre-training, our model exhibits significant performance improvements across a wide range of code retrieval benchmarks. |
| title | ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search |
| topic | Computation and Language Information Retrieval Software Engineering |
| url | https://arxiv.org/abs/2403.16702 |