Fighting Redundancy and Model Decay with Embeddings

Understand

Every day, hundreds of millions of new Tweets containing over 40 languages of ever-shifting vernacular flow through Twitter.

• Models that attempt to extract insight from this firehose of information must face the torrential covariate shift that is endemic to the Twitter platform.
• While regularly-retrained algorithms can maintain performance in the face of this shift, fixed model features that fail to represent new trends and tokens can quickly become stale, resulting in performance degradation.
• To mitigate this problem we employ learned features, or embedding models, that can efficiently represent the most relevant aspects of a data distribution.