Confusion Network (CN) Decoding System

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A Confusion Network (CN) Decoding System is a Natural Language Processing System that can build a Word Confusion Network from the output data of automatic speech recognition or machine translation systems.



References

2021

2020

2020 JointlyEncodingWordConfusionNet Fig1.png
Figure 1: An overview of our proposed WCN-BERT SLU, which contains a BERT encoder, an utterance representation model, and an output layer. First, the WCN and the last system act are arranged as a sequence to be fed into the BERT encoder. The token-level BERT outputs are then integrated into an utterance-level vector representation. Finally, either a discriminative (semantic tuple classifier, STC) or generative (hierarchical decoder, HD) approach is utilized in the output layer for predicting the act-slot-value triplets.

2011

2008a

2008b

2007

2005