ReFr: An Open-Source Reranker Framework
Venue
Interspeech 2013, pp. 756-758
Publication Year
2013
Authors
Daniel M. Bikel, Keith B. Hall
BibTeX
Abstract
ReFr (http://refr.googlecode.com) is a
software architecture for specifying, training and using reranking models, which
take the n-best output of some existing system and produce new scores for each of
the n hypotheses that potentially induce a different ranking, ideally yielding
better results than the original system. The Reranker Framework has some special
support for building discriminative language models, but can be applied to any
reranking problem. The framework is designed with parallelism and scalability in
mind, being able to run on any Hadoop cluster out of the box. While extremely
efficient, ReFr is also quite flexible, allowing researchers to explore a wide
variety of features and learning methods. ReFr has been used for building
state-of-the-art discriminative LM’s for both speech recognition and machine
translation systems.
