Invention Application
- Patent Title: SEQUENCE TRANSDUCTION NEURAL NETWORKS
-
Application No.: US16746012Application Date: 2020-01-17
-
Publication No.: US20200151398A1Publication Date: 2020-05-14
- Inventor: Lei Yu , Christopher James Dyer , Tomas Kocisky , Philip Blunsom
- Applicant: DeepMind Technologies Limited
- Main IPC: G06F40/44
- IPC: G06F40/44 ; G06F40/16 ; G06N3/04 ; G06F17/18

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from an input sequence. In one aspect, a method comprises maintaining a set of current hypotheses, wherein each current hypothesis comprises an input prefix and an output prefix. For each possible combination of input and output prefix length, the method extends any current hypothesis that could reach the possible combination to generate respective extended hypotheses for each such current hypothesis; determines a respective direct score for each extended hypothesis using a direct model; determines a first number of highest-scoring hypotheses according to the direct scores; rescores the first number of highest-scoring hypotheses using a noisy channel model to generate a reduced number of hypotheses; and adds the reduced number of hypotheses to the set of current hypotheses.
Public/Granted literature
- US11423237B2 Sequence transduction neural networks Public/Granted day:2022-08-23
Information query