Paper: Low-Dimensional Discriminative Reranking

ACL ID N12-1088
Title Low-Dimensional Discriminative Reranking
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012
Authors

The accuracy of many natural language pro- cessing tasks can be improved by a reranking step, which involves selecting a single output from a list of candidate outputs generated by a baseline system. We propose a novel fam- ily of reranking algorithms based on learning separate low-dimensional embeddings of the task?s input and output spaces. This embed- ding is learned in such a way that prediction becomes a low-dimensional nearest-neighbor search, which can be done computationally ef- ficiently. A key quality of our approach is that feature engineering can be done separately on the input and output spaces; the relationship between inputs and outputs is learned auto- matically. Experiments on part-of-speech tag- ging task in four languages show significant improvements over a baseline dec...