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arXiv · 2001.05540

Insertion-Deletion Transformer

Laura Ruis·Mitchell Stern·Julia Proskurnia·William Chan
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Abstract

We propose the Insertion-Deletion Transformer, a novel transformer-based neural architecture and training method for sequence generation. The model consists of two phases that are executed iteratively, 1) an insertion phase and 2) a deletion phase. The insertion phase parameterizes a distribution of insertions on the current output hypothesis, while the deletion phase parameterizes a distribution of deletions over the current output hypothesis. The training method is a principled and simple algorithm, where the deletion model obtains its signal directly on-policy from the insertion model output. We demonstrate the effectiveness of our Insertion-Deletion Transformer on synthetic translation tasks, obtaining significant BLEU score improvement over an insertion-only model.

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Laura Ruis, Mitchell Stern, Julia Proskurnia, William Chan. 2020-01-15. Insertion-Deletion Transformer. https://arxiv.org/abs/2001.05540

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