Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
Table 2: Percentage of different pronoun types.
['[BOLD] Pronoun', '[BOLD] Subtitles', '[BOLD] Europarl', '[BOLD] TED Talks']
We find the smallest window within which a referential English pronoun is resolved by an antecedent or postcedent using NeuralCoref. Further, anaphoric pronouns are much more frequent compared to cataphoric ones across all three corpora. This finding motivates us to investigate the performance of a context-aware NMT model (trained on Subtitles) for the translation of cataphoric pronouns.