Abstract
For some language pairs, pronoun translation is a discourse-driven task which requires information that lies beyond its local context. This motivates the task of predicting the correct pronoun given a source sentence and a target translation, where the translated pronouns have been replaced with placeholders. For cross-lingual pronoun prediction, we suggest a neural network-based model using preceding nouns and determiners as features for suggesting antecedent candidates. Our model scores on par with similar models while having a simpler architecture.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Second Workshop on Discourse in Machine Translation |
| Publication date | 21 Sept 2015 |
| ISBN (Print) | 978-1-941643-32-7 |
| DOIs | |
| Publication status | Published - 21 Sept 2015 |
| Externally published | Yes |
Keywords
- pronoun translation
- cross-lingual pronoun prediction
- anaphora resolution
- neural networks
- discourse context
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