  We apply sequence-to-sequence model to mitigate the impact of speech
recognition errors on open domain end-to-end dialog generation. We cast the
task as a domain adaptation problem where ASR transcriptions and original text
are in two different domains. In this paper, our proposed model includes two
individual encoders for each domain data and make their hidden states similar
to ensure the decoder predict the same dialog text. The method shows that the
sequence-to-sequence model can learn the ASR transcriptions and original text
pair having the same meaning and eliminate the speech recognition errors.
Experimental results on Cornell movie dialog dataset demonstrate that the
domain adaption system help the spoken dialog system generate more similar
responses with the original text answers.
