We present a new model called LatticeRnn, which generalizes recurrent neural networks (RNNs) to process weighted lattices as input, instead of sequences. A LatticeRnn can encode the complete structure of a lattice into a dense representation, which …
It is common knowledge that translation is an ambiguous, 1-to-n mapping process, but to date, our community has produced no empirical estimates of this ambiguity. We have developed an annotation tool that enables us to create representations that …
We describe a new approach for rescoring speech lattices - with long-span language models or wide-context acoustic models - that does not entail computationally intensive lattice expansion or limited rescoring of only an N-best list. We view the set …