Conversational data has a unique structure—call logs and transcripts are hierarchical, with each statement relying on the order and content of the ones before. In this talk, we discuss ways to create effective call transcript classifiers that leverage automated data labeling and synthetic data generation.
Jaxon’s approach to handling call transcript data involves a 3-component neural model:
- Text representation
- Attention layer (coordinate different utterances in a single dialogue)
- Classification
Also, we show how Jaxon can help you create models faster with techniques like unsupervised data augmentation—without compromising the model’s integrity.
