  We present the InterviewBot that dynamically integrates conversation history
and customized topics into a coherent embedding space to conduct 10 mins
hybrid-domain (open and closed) conversations with foreign students applying to
U.S. colleges for assessing their academic and cultural readiness. To build a
neural-based end-to-end dialogue model, 7,361 audio recordings of
human-to-human interviews are automatically transcribed, where 440 are manually
corrected for finetuning and evaluation. To overcome the input/output size
limit of a transformer-based encoder-decoder model, two new methods are
proposed, context attention and topic storing, allowing the model to make
relevant and consistent interactions. Our final model is tested both
statistically by comparing its responses to the interview data and dynamically
by inviting professional interviewers and various students to interact with it
in real-time, finding it highly satisfactory in fluency and context awareness.
