Translating Pronouns with Latent Anaphora Resolution
Christian Hardmeier, Jörg Tiedemann, Joakim Nivre · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2014
We discuss the translation of anaphoric pronouns in statistical machine translation from English into French. Pronoun translation requires resolving the antecedents of the pronouns in the input, a classic discourse processing problem that is usually approached through supervised learning from manually annotated data. We cast cross-lingual pronoun prediction as a classification task and present a neural network architecture that incorporates the links between anaphors and potential antecedents as latent variables, allowing us to train the classifier on parallel text without explicit supervision for the anaphora resolver. We demonstrate that our approach works just as well for classification as using an external coreference resolver whereas its impact in a practical translation experiment is more limited.