Papers › Data-to-text Generation by Splicing Together Nearest Neighbors

Data-to-text Generation by Splicing Together Nearest Neighbors

20 Jan 2021EMNLP 2021 11arXiv:2101.08248archive 2025-07-28

Sam Wiseman, Arturs Backurs, Karl Stratos

We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs. Unlike recent work that conditions on retrieved neighbors but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor text, by inserting or replacing them in partially constructed generations. Standard techniques for training such a policy require an oracle derivation for each generation, and we prove that finding the shortest such derivation can be reduced to parsing under a particular weighted context-free grammar. We find that policies learned in this way perform on par with strong baselines in terms of automatic and human evaluation, but allow for more interpretable and controllable generation.

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