Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-sequence Model
Table 5: Compared to test.pred, the results of our dual-encoder sequence to sequence model on test_asr.enc.
['Loss functions', '[BOLD] Training data', '[BOLD] BLEU Score']
In this experiment, we train Seq2Seq-text first and then build another encoder for ASR transcription inputs to minimize the state vectors of the two encoders by Eq. (2). For testing, test_asr.enc is used as the encoder input. The ASR gate will allow the ASR state vector to be passed into the decoder. The model gets 0.25 BLEU score by only using 20% train_asr.enc and its original texts pair for training. It can be applied to the real world scenario lacking ASR transcription data. With more ASR data, the performance gradually improves and reaches the best BLEU score of 0.269 when utilizing all train_asr.enc. To be more advanced, we combine the two loss functions to optimize our model. It means that we also fine-tune the decoder on the target domain, in addition to minimizing the state vectors of the encoders.