Generating (Formulaic) Text by Splicing Together Nearest Neighbors
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We propose to tackle conditional text generation tasks, especially those which require generating formulaic text, by splicing together segments of text from retrieved "neighbor" source-target pairs. Unlike recent work that conditions on retrieved neighbors in an encoder-decoder setting but generates text token-by-token, left-to-right, we learn a policy that directly manipulates segments of neighbor text (i.e., by inserting or replacing them) to form an output. 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 allow for interpretable table-to-text or headline generation that is competitive with neighbor-based token-level policies on automatic metrics, though on all but one dataset neighbor-based policies underperform a strong neighborless baseline. In all cases, however, generating by splicing is faster.
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