Massive-scale Decoding for Text Generation using Lattices

Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options. We present a search algorithm to construct lattices encoding a massive number of generation options. First, we restructure decoding as a...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Xu, Jiacheng, Siddhartha Reddy Jonnalagadda, Durrett, Greg
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.05.2022
Subjects
Algorithms
Lattices
Machine translation
Search algorithms
Online AccessGet full text

Cover

More Information
Summary:Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options. We present a search algorithm to construct lattices encoding a massive number of generation options. First, we restructure decoding as a best-first search, which explores the space differently than beam search and improves efficiency by avoiding pruning paths. Second, we revisit the idea of hypothesis recombination: we can identify pairs of similar generation candidates during search and merge them as an approximation. On both summarization and machine translation, we show that our algorithm encodes thousands of diverse options that remain grammatical and high-quality into one lattice. This algorithm provides a foundation for building downstream generation applications on top of massive-scale diverse outputs.
ISSN:2331-8422