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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

TaskDatasetResultRank
Language modellingLM1B (test)
Perplexity142.9
120
Language ModelingOne Billion Word Benchmark (test)
Test Perplexity142.9
108
Text GenerationLM1B (test)--
72
Masked Language ModelingXSUM randomly sampled
U-PPL3.8
20
Masked Language ModelingSNLI (randomly sampled)
PPL (U)9.5
20
Unconditional Text GenerationUnconditional Text Generation
BLEU28.67
11
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