Dual-Normalization
时间: 2023-12-27 20:03:38 浏览: 25
Dual-normalization is a technique used in natural language processing (NLP) to improve the performance of language models. It involves normalizing the input data and the output data of the model separately.
In traditional normalization, the input data and output data are normalized using the same method. However, this can lead to problems when dealing with text data, as the input data (i.e., the text) may be normalized in a way that is different from the output data (i.e., the predicted text).
Dual-normalization addresses this issue by normalizing the input and output data using different methods. For example, the input data may be lowercased and stripped of punctuation, while the output data may only be lowercased.
This technique has been shown to improve the performance of language models, particularly in tasks such as machine translation and text summarization.