|Date||Apr 24, 2015|
|Title||Pronominal Anaphora in Statistical Machine Translation|
Translating pronouns is a challenge that current machinetranslation systems are ill-prepared to handle because it requires complexcross-sentence dependencies in the target language and because the pronounsoften can't be translated literally even in a very literal translation style.My talk is going to focus on the problem of modelling pronominal anaphora inthe framework of phrase-based SMT. Pronoun translation is first cast as aclassification task that is separate from SMT. I describe a neural networkclassifier for this task that models anaphoric links as latent variables andcan be trained on parallel bitexts without explicit coreference annotations.Then, I present Docent, my document-level local search decoder for phrase-basedSMT, and show some results from the integration of the anaphora classifier inthis framework.
|Bio||Christian Hardmeier is a post-doctoral researcher at Uppsala University in Sweden,where he also completed his PhD in 2014 with an award-winning thesis onDiscourse in Statistical Machine Translation. Before coming to Uppsala, heworked with the MT group at FBK in Trento (Italy) after obtaining an M.A. inNordic philology from the University of Basel (Switzerland). His currentresearch still focuses on both the linguistic and the technical aspects ofcross-sentence, discourse-level problems in machine translation.|