  Average hold time is a concern for call centers because it affects customer
satisfaction. Contact centers should instruct their agents to use special
on-hold scripts to maintain positive interactions with clients. This study
presents a natural language processing model that detects on-hold phrases in
customer service calls transcribed by automatic speech recognition technology.
The task of finding hold scripts in dialogue was formulated as a multiclass
text classification problem with three mutually exclusive classes: scripts for
putting a client on hold, scripts for returning to a client, and phrases
irrelevant to on-hold scripts. We collected an in-house dataset of calls and
labeled each dialogue turn in each call. We fine-tuned RuBERT on the dataset by
exploring various hyperparameter sets and achieved high model performance. The
developed model can help agent monitoring by providing a way to check whether
an agent follows predefined on-hold scripts.
