Massive-scale Decoding for Text Generation using Lattices
TL;DR: a new search algorithm to construct lattices encoding many generation options; two key technical contributions: (1) best-first search, (2) path recombination.
Visualization
We provide a few examples in the vis
folder and on my homepage. You need to download the html files to view and interact with the model outputs.
The complete set of outputs are available on Box.
Getting started
model
contains all of the methods, including baselines like beam search, nucleus sampling, and our methods.evaluation
contains scripts for evaluation.command
are the prompts and shells we use to run the experiment.
Beam Search:
PYTHONPATH=./ python src/recom_search/command/run_pipeline.py -nexample 100 -ngram_suffix 4 -beam_size 16 -min_len 10 -max_len 35 -model bs
Best-first Search:
PYTHONPATH=./ python src/recom_search/command/run_pipeline.py -nexample 100 -ngram_suffix 4 -beam_size 16 -min_len 10 -max_len 35 -model astar_baseline
Best-first Search with Recomb:
PYTHONPATH=./ python src/recom_search/command/run_pipeline.py -nexample 100 -ngram_suffix 4 -beam_size 16 -min_len 10 -max_len 35 -model astar -merge imp -avg_score 0.75 -adhoc
Best-first Search with Zip:
PYTHONPATH=./ python src/recom_search/command/run_pipeline.py -nexample 100 -ngram_suffix 4 -beam_size 16 -min_len 10 -max_len 35 -model astar -merge zip -avg_score 0.75 -adhoc
More detailed instructions coming soon!
Citation
@misc{xu-durrett-2021-massive,
title={Massive-scale Decoding for Text Generation using Lattices},
author={Jiacheng Xu and Greg Durrett},
year={2021},
eprint={2112.07660},
archivePrefix={arXiv},
primaryClass={cs.CL}
}