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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 ModelingWikiText-103 (test)
Perplexity20.42
773
Language modellingLM1B (test)
Perplexity142.9
206
Language ModelingOne Billion Word Benchmark (test)
Test Perplexity142.9
125
Text GenerationLM1B (test)--
90
Language ModelingLM1B
Perplexity142.9
65
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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