INTERSPEECH 2013
14thAnnual Conference of the International Speech Communication Association

Lyon, France
August 25-29, 2013

ReFr: An Open-Source Reranker Framework

Daniel M. Bikel, Keith B. Hall

Google, USA

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.

Full Paper

Bibliographic reference.  Bikel, Daniel M. / Hall, Keith B. (2013): "Refr: an open-source reranker framework", In INTERSPEECH-2013, 756-758.