BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model
About
We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high-quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality.
Alex Wang, Kyunghyun Cho• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Language modelling | LM1B (test) | Perplexity142.9 | 120 | |
| Language Modeling | One Billion Word Benchmark (test) | Test Perplexity142.9 | 108 | |
| Text Generation | LM1B (test) | -- | 72 | |
| Masked Language Modeling | XSUM randomly sampled | U-PPL3.8 | 20 | |
| Masked Language Modeling | SNLI (randomly sampled) | PPL (U)9.5 | 20 | |
| Unconditional Text Generation | Unconditional Text Generation | BLEU28.67 | 11 |
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