Header image by Jelleke Vanooteghem on Unsplash
Better Language Models, Automated training (with Kubeflow), Keyboard Hints, Interface Insights, Message Tags and Research & survey
Earlier this year we’ve experimented with advanced transformer models like GPT-2 for text generation.
At Deepdesk we have multiple machine learning pipelines per customer. These pipelines include multiple steps like preprocessing, clustering and training.
We introduce keyboard hints to promote the tab or arrow keys for selecting suggestions. This allows agents to keep their hands on the keyboard, and select suggestions faster. With faster selection we anticipate increased overall usage.
In order to get a better understanding of how agents select suggestions we implemented some additional tracking. For every used suggestion we can now see if this was done with tab, arrow keys or mouse, allowing for optimisation in how we offer suggestions.🧑🏽💻
We redesigned our data model to store message properties more flexible. With new requirements, new platforms or additional message details we needed a more flexible model to store the details. Tags are used to determine which model to use, and can be used for selecting which conversations to train on.🍒
We have conducted an extensive user survey in combination with core product research. We will use the valuable feedback on user experience and actionable insights to determine our future roadmap and create additional improvements for our core product. Thanks to all who participated. 🙏🏼
Read more in our April 2020 post 🤓
Header image by Jelleke Vanooteghem on Unsplash