Pawe{\l} M{\k{a}}ka

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models

Achieving human-level translations requires leveraging context to ensure coherence and handle complex phenomena like pronoun disambiguation. Sparsity of contextually rich examples in the standard training data has been hypothesized as the reason for the difficulty of context utilization. In this wor