Code for Massive-scale Decoding for Text Generation using Lattices

Last update: Dec 18, 2022

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Overview

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}
}

Contact

[email protected]

Code for Massive-scale Decoding for Text Generation using Lattices

Related tags

Overview

Massive-scale Decoding for Text Generation using Lattices

Visualization

Getting started

Citation

Contact

Owner

Jiacheng Xu

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Code for Massive-scale Decoding for Text Generation using Lattices

Related tags

Overview

Massive-scale Decoding for Text Generation using Lattices

Visualization

Getting started

Citation

Contact

Owner

Jiacheng Xu

Python KNN model: Predicting a probability of getting a work visa. Tableau: Non-immigrant visas over the years.

Unofficial PyTorch implementation of Google AI's VoiceFilter system

Cross Quality LFW: A database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

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Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into tables through jointly extracting intervention, outcome and outcome measure entities and their relations.

3D ResNet Video Classification accelerated by TensorRT

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

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Per-Pixel Classification is Not All You Need for Semantic Segmentation

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