GEN Encoder learns query intent embeddings from click logs as weak supervision and multi-task paraphrase training, outperforming prior methods on intent similarity and using nearest-neighbor search to cover half of unseen queries.
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Pith papers citing it
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2019 2verdicts
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Presents a distributed infrastructure for scaling skip-gram graph embeddings to 68M-vertex networks by avoiding partitioning, using dynamic size-constrained graphs, and efficient indexing for updates.
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Generic Intent Representation in Web Search
GEN Encoder learns query intent embeddings from click logs as weak supervision and multi-task paraphrase training, outperforming prior methods on intent similarity and using nearest-neighbor search to cover half of unseen queries.
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Graph Embeddings at Scale
Presents a distributed infrastructure for scaling skip-gram graph embeddings to 68M-vertex networks by avoiding partitioning, using dynamic size-constrained graphs, and efficient indexing for updates.