Papers › Cloze-driven Pretraining of Self-attention Networks
Cloze-driven Pretraining of Self-attention Networks
Alexei Baevski, Sergey Edunov, Yinhan Liu, Luke Zettlemoyer, Michael Auli
We present a new approach for pretraining a bi-directional transformer model that provides significant performance gains across a variety of language understanding problems. Our model solves a cloze-style word reconstruction task, where each word is ablated and must be predicted given the rest of the text. Experiments demonstrate large performance gains on GLUE and new state of the art results on NER as well as constituency parsing benchmarks, consistent with the concurrently introduced BERT model. We also present a detailed analysis of a number of factors that contribute to effective pretraining, including data domain and size, model capacity, and variations on the cloze objective.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Constituency Parsing | Penn Treebank | CNN Large + fine-tune | F1 score | 95.6 | #12 of 27 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (English) | CNN Large + fine-tune | F1 | 93.5 | #18 of 73 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | CNN Large | Accuracy | 94.6 | #33 of 87 | Archive leaderboard | report |
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Methods
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