Dual Learning for Semi-Supervised Natural Language Understanding
TABLE V: Evaluations of the SSG model for the dual task of NLU, which is supervised by full training sets on ATIS and SNIPS respectively.
['[BOLD] Model', '[BOLD] ATIS BLEU', '[BOLD] ATIS Slot Acc', '[BOLD] SNIPS BLEU', '[BOLD] SNIPS Slot Acc']
From the result of “(-) w/o feeding intent” row, we can observe that the intent is essential for BLEU scores, whereas intent detection is much easy in NLU. The global-level BLSTM in the encoder, copy mechanism and SC-LSTM cell in the decoder are also important components of the SSG model.