Representation is King: The Journey to Quality Dialog Embeddings
2024-07-12 , Terrace 2A

In natural language processing, embeddings are crucial for understanding textual data. In this talk, we’ll explore sentence embeddings and their application in dialog systems. We'll focus on a use case involving the classification of dialogs.

We'll demonstrate the necessity of sentence transformers for this problem, specifically utilizing one of the top-performing small-sized sentence transformers. We will show how to fine-tune this model with both labeled and unlabeled dialog data, using the SentenceTransformers Python framework.

This talk is practical, packed with easy-to-follow examples, and aimed at building intuition around this topic. While some basic knowledge of Transformers would be beneficial, it is not required. Newcomers are also welcome.


Expected audience expertise:

Intermediate

See also:

At Salted CX, my role as a Machine Learning engineer revolves around specializing in Natural Language Processing. Specifically, I utilize Transformer models to gain insights into the operations of contact centers, enhancing visibility and understanding. I hold an Engineering Doctoral (EngD) degree in Data Science from the Technical University of Eindhoven.