CPC G06F 16/3329 (2019.01) [G06F 16/9024 (2019.01); G06F 17/16 (2013.01); G06F 17/18 (2013.01); G06F 40/30 (2020.01); G06N 3/04 (2013.01); G06N 20/00 (2019.01)] | 15 Claims |
1. A method comprising:
obtaining contextualized passage embeddings and contextualized answer embeddings for a text pair;
obtaining a passage embedding matrix;
constructing a corresponding passage graph based on said passage embedding matrix;
applying a bidirectional gated graph neural network to said corresponding passage graph until a final state embedding is determined, during which application intermediate node embeddings are fused from both incoming and outgoing edges of said graph;
obtaining a graph-level embedding from said final state embedding;
decoding said final state embedding to generate an output sequence; and
training a machine learning model to generate at least one question corresponding to said text pair by evaluating said output sequence.
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