Pretrained Language Models (LMs) generate text with remarkable quality, novelty,and coherence. Yet applying LMs to the problems of paraphrasing and infilling currently requires direct supervision, since these tasks break the left-to-right generation setup of pretrained LMs. We present Reflective Decoding, a novel unsupervised approach to apply the capabilities of pretrained LMs to non-sequential tasks. Our approach is general and applicable to two distant tasks - paraphrasing and abductive reasoning. It requires no supervision or  parallel corpora, only two pretrained language models : forward and backward. Reflective Decoding operates in two intuitive steps. In the contextualization step, we use LMs to generate many left and right contexts which collectively capture the meaning of the input sentence. Then , in the reflection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM. We reflect through the generated contexts, effectively using themas an intermediate meaning representation to generate conditional output. Empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding introduces the concept of using generated contexts to represent meaning, opening up new possibilities for unsupervised conditional text generation .
Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions : forward and backward. First, in the contextualization step, we use LMs to generate ensembles of past and future contexts which collectively capture the input (e.g. the source sentence for paraphrasing). Second , in the reflection step , we condition on these "context ensembles", generating outputs that are compatible with them. Comprehensive empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding surpasses multiple supervised baselines on various metrics including human evaluation .
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Pretrained Language Models
Publicly available, large pretrained LanguageModels
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novelty,and coherence. Yet applying LMs to the problems of paraphrasing and infilling currently requires direct supervision, since these tasks break the left-to-right generation setup of pretrained LMs. We
but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we
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approach to apply the capabilities of pretrained
algorithm that allows for direct application of unidirectional
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approach is general and applicable to two distant tasks - paraphrasing and abductive reasoning. It
2-step approach
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even
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pretrained language models
off-the-shelf pretrained LMs in opposite directions
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Reflective Decoding operates in two intuitive steps. In
First, in
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many left and right
ensembles of past and future
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meaning of the input sentence. Then
input (e.g. the source sentence for paraphrasing). Second
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we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM. We reflect through the generated contexts, effectively using themas an intermediate meaning representation to generate conditional output. Empirical
, we condition on these "context ensembles", generating outputs that are compatible with them. Comprehensive empirical
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introduces the concept of using generated contexts to represent meaning, opening up new possibilities for unsupervised conditional text generation
surpasses multiple supervised baselines on various metrics including human evaluation