To analyze how neural machine translation (NMT) models represent and translate idioms, researchers vary the amount of surrounding context available to the encoder using three context-masking conditions:

1. Full Context: The idiom phrase is encoded normally within the entire input sentence, allowing the model to use surrounding syntactic and semantic clues.
2. Phrase-Level Context: The idiom phrase is encoded in isolation (with the surrounding sentence context masked or removed), forcing the model to represent the idiom solely based on its constituent words.
3. Word-Level Context: Each word of the idiom is encoded independently, breaking phrase-level cohesion and forcing a compositional, word-by-word representation.

By feeding these encoder representations to the decoder, researchers can measure how varying context affects decoder distributions and translation output.