Abstract / Description of output
We present a maximum entropy classifier
for cross-lingual pronoun prediction. The
features are based on local source- and
target-side contexts and antecedent information obtained by a co-reference resolution system. With only a small set of
feature types our best performing system
achieves an accuracy of 72.31%. According to the shared task’s official macro-
averaged F1-score at 57.07%, we are
among the top systems, at position three
out of 14. Feature ablation results show
the important role of target-side information in general and of the resolved target-side antecedent in particular for predicting
the correct classes.
Original language | English |
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Title of host publication | Proceedings of the Second Workshop on Discourse in Machine Translation |
Place of Publication | Lisbon, Portugal |
Publisher | Association for Computational Linguistics |
Pages | 115-121 |
Number of pages | 7 |
Publication status | Published - 1 Sept 2015 |