Mitigating the Impact of Speech Recognition Errors on Chatbot using Sequence-to-sequence Model
Table 4: Compared to test.pred, the result on test_asr.enc using different percentages of ASR data to fine-tune.
['[BOLD] Model', '[BOLD] Fine-tuning data', '[BOLD] BLEU Score']
To solve the ASR error problem, we experiment with a common domain algorithm: fine-tuning Seq2Seq-text on ASR transcriptions. In this method, the model can learn the semantic meanings of original texts first and then adapt itself to data from the ASR domain. For the real world scenario, the original text data are much more than the ASR transcription data. Therefore, we fine-tune our model on different percentages of train_asr.enc to examine its performance when fewer data are available.