Methods › Natural Language Processing › Autoencoding Transformers › ConvBERT

ConvBERT

5 papers tagged archive 2025-07-28

Introduced by Zi-Hang Jiang et al. in ConvBERT: Improving BERT with Span-based Dynamic Convolution

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ConvBERT is a modification on the BERT architecture which uses a span-based dynamic convolution to replace self-attention heads to directly model local dependencies. Specifically a new mixed attention module replaces the self-attention modules in BERT, which leverages the advantages of convolution to better capture local dependency. Additionally, a new span-based dynamic convolution operation is used to utilize multiple input tokens to dynamically generate the convolution kernel. Lastly, ConvBERT also incorporates some new model designs including the bottleneck attention and grouped linear operator for the feed-forward module (reducing the number of parameters).

PaperSource

Papers archive 2025-07-28

5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

12 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Automatic Speech Recognition1
Automatic Speech Recognition (ASR)1
Bias Detection1
Drug Discovery1
Language Modeling1
Language Modelling1
Misinformation1
Natural Language Understanding1
Punctuation Restoration1
Speech Recognition1
Transfer Learning1
speech-recognition1

Usage over time archive 2025-07-28

Papers per year tagged with ConvBERT: 2020 to 2025, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023 2024: 1 paper 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (5 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Autoencoding TransformersTransformers

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