Data-to-text Generation by Splicing Together Nearest Neighbors
Sam Wiseman, Artūrs Bačkurs, Karl Stratos · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" sourcetarget 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.