This project focuses on advancing the state-of-the-art in language processing with recurrent neural networks. We are currently applying these to language modeling, machine translation, speech recognition, language understanding and meaning representation. A special interest in is adding side-channels of information as input, to model phenomena which are not easily handled in other frameworks.
A toolkit for doing RNN language modeling with side-information is in the associated download. Sample word vectors for use with this toolkit can be found here (be sure to unzip), along with training and test scripts. These are for Penn Treebank words, and achieve a perplexity of 128; removing the context dependence results in a perplexity of 144.
As described in the NAACL-2013 paper “Linguistic Regularities in Continuous Space Word Representations,” we have found that the word representations capture many linguistic regularities. A test set for quantifying the degree to which syntactic regularities are modeled can be found here.