Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Bandits

Understand

Training data for machine translation (MT) is often sourced from a multitude of large corpora that are multi-faceted in nature, e.g.

• containing contents from multiple domains or different levels of quality or complexity.
• Naturally, these facets do not occur with equal frequency, nor are they equally important for the test scenario at hand.
• In this work, we propose to optimize this balance jointly with MT model parameters to relieve system developers from manual schedule design.