Banking call centers receive millions of calls annually, with much of the
information in these calls unavailable to analysts interested in tracking new
and emerging call center trends. In this study we present an approach to call
center theme detection that captures the occurrence of multiple themes in a
question, using a publicly available corpus of StackExchange personal finance
questions, labeled by users with topic tags, as a testbed. To capture the
occurrence of multiple themes in a single question, the approach encodes and
clusters at the sentence- rather than question-level. We also present a
comparison of state-of-the-art sentence encoding models, including the SBERT
family of sentence encoders. We frame our evaluation as a multiclass
classification task and show that a simple combination of the original sentence
text, Universal Sentence Encoder, and KMeans outperforms more sophisticated
techniques that involve semantic parsing, SBERT-family models, and HDBSCAN. Our
highest performing approach achieves a Micro-F1 of 0.46 for this task and we
show that the resulting clusters, even when slightly noisy, contain sentences
that are topically consistent with the label associated with the cluster.

本文提出了一种通话中心主题检测的方法，该方法捕捉到一个问题中多个主题的发生，并提出了一种以句子而非问题为级别的编码和聚类方法，以及一种多类分类任务的比较，表明了所提出的方法在其上表现出色。