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Song et al., 2021 - Google Patents

Structural information preserving for graph-to-text generation

Song et al., 2021

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Document ID
15604746734803348809
Author
Song L
Wang A
Su J
Zhang Y
Xu K
Ge Y
Yu D
Publication year
Publication venue
arXiv preprint arXiv:2102.06749

External Links

Snippet

The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or even drop the core structural information of input graphs when generating outputs. We propose to …
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Classifications

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    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G06F17/30675—Query execution
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    • G06—COMPUTING; CALCULATING; COUNTING
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    • G06F17/2765—Recognition
    • G06F17/277—Lexical analysis, e.g. tokenisation, collocates
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    • G06—COMPUTING; CALCULATING; COUNTING
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