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In your paper, you use a unidirectional ON-LSTM to trained a language model and then phrase grammar with the output distance of the pretrained language model. How can we explain that the level of first token is independent with the future tokens? Is there any bidirectional way to do it?
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I think you can try bidirection language model (e.g. elmo) or masked language model (e.g. bert). But the perplexity won't be comparable to previous language models.
In your paper, you use a unidirectional ON-LSTM to trained a language model and then phrase grammar with the output distance of the pretrained language model. How can we explain that the level of first token is independent with the future tokens? Is there any bidirectional way to do it?
The text was updated successfully, but these errors were encountered: