Kavli Affiliate: Jia Liu
| First 5 Authors: Weihong Du, Jia Liu, Zujie Wen, Dingnan Jin, Hongru Liang
| Summary:
It is time-saving to build a reading assistant for customer service
representations (CSRs) when reading user manuals, especially information-rich
ones. Current solutions don’t fit the online custom service scenarios well due
to the lack of attention to user questions and possible responses. Hence, we
propose to develop a time-saving and careful reading assistant for CSRs, named
CARE. It can help the CSRs quickly find proper responses from the user manuals
via explicit clue chains. Specifically, each of the clue chains is formed by
inferring over the user manuals, starting from the question clue aligned with
the user question and ending at a possible response. To overcome the shortage
of supervised data, we adopt the self-supervised strategy for model learning.
The offline experiment shows that CARE is efficient in automatically inferring
accurate responses from the user manual. The online experiment further
demonstrates the superiority of CARE to reduce CSRs’ reading burden and keep
high service quality, in particular with >35% decrease in time spent and
keeping a >0.75 ICC score.
| Search Query: ArXiv Query: search_query=au:”Jia Liu”&id_list=&start=0&max_results=3