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Found 6 papers, 1 papers with code
A Case Study of User Communication Styles with Customer Service Agents versus Intelligent Virtual Agents
Of those IVA conversations, 8, 324 escalated to human live chat agents.
An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale
Mining the latent intentions from large volumes of natural language inputs is a key step to help data analysts design and refine Intelligent Virtual Assistants (IVAs) for customer service and sales support.
A Semi-Supervised Deep Clustering Pipeline for Mining Intentions From Texts
The clustering quality exceeds the state-of-the-art results when BERT is fine-tuned with labeled subsets of only 2. 5% of the task data.
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
Such interpretability can be highly advantageous in applications requiring transparency of model outputs or where users desire to inject prior knowledge of time-series patterns into the generative model.
Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis
In (Yang et al. 2016), a hierarchical attention network (HAN) is created for document classification.
An Annotated Corpus of Relational Strategies in Customer Service
We create and release the first publicly available commercial customer service corpus with annotated relational segments.
