  Is chatbot able to completely replace the human agent? The short answer could
be - "it depends...". For some challenging cases, e.g., dialogue's topical
spectrum spreads beyond the training corpus coverage, the chatbot may
malfunction and return unsatisfied utterances. This problem can be addressed by
introducing the Machine-Human Chatting Handoff (MHCH), which enables
human-algorithm collaboration. To detect the normal/transferable utterances, we
propose a Difficulty-Assisted Matching Inference (DAMI) network, utilizing
difficulty-assisted encoding to enhance the representations of utterances.
Moreover, a matching inference mechanism is introduced to capture the
contextual matching features. A new evaluation metric, Golden Transfer within
Tolerance (GT-T), is proposed to assess the performance by considering the
tolerance property of the MHCH. To provide insights into the task and validate
the proposed model, we collect two new datasets. Extensive experimental results
are presented and contrasted against a series of baseline models to demonstrate
the efficacy of our model on MHCH.
