Code for Massive-scale Decoding for Text Generation using Lattices

Related tags

Deep Learninglattice-generation
Overview

Massive-scale Decoding for Text Generation using Lattices

Jiacheng Xu, Greg Durrett

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]

Owner
Jiacheng Xu
Fifth-year PhD @ UT Austin
Jiacheng Xu
GitHub Repository
People log into different sites every day to get information and browse through these sites one by one

HyperLink People log into different sites every day to get information and browse through these sites one by one. And they are exposed to advertisemen

0 Feb 17, 2022
Add-on for importing and auto setup of character creator 3 character exports.

CC3 Blender Tools An add-on for importing and automatically setting up materials for Character Creator 3 character exports. Using Blender in the Chara

260 Jan 05, 2023
An excellent hash algorithm combining classical sponge structure and RNN.

SHA-RNN Recurrent Neural Network with Chaotic System for Hash Functions Anonymous Authors [摘要] 在这次作业中我们提出了一种新的 Hash Function —— SHA-RNN。其以海绵结构为基础，融合了混

Houde Qian 5 May 15, 2022
PyTorch implementation of Soft-DTW: a Differentiable Loss Function for Time-Series in CUDA

Soft DTW Loss Function for PyTorch in CUDA This is a Pytorch Implementation of Soft-DTW: a Differentiable Loss Function for Time-Series which is batch

Keon Lee 76 Dec 20, 2022
Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21)

AdvRush Official Code for AdvRush: Searching for Adversarially Robust Neural Architectures (ICCV '21) Environmental Set-up Python == 3.6.12, PyTorch =

11 Dec 10, 2022
Dialect classification

Dialect-Classification This repository presents the data that was used in a talk at ICKL-5 (5th International Conference on Kurdish Linguistics) at th

Kurdish-BLARK 0 Nov 12, 2021
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

187 Dec 26, 2022
ML-based medical imaging using Azure

Disclaimer This code is provided for research and development use only. This code is not intended for use in clinical decision-making or for any other

Microsoft Azure 68 Dec 23, 2022
Moer Grounded Image Captioning by Distilling Image-Text Matching Model

Moer Grounded Image Captioning by Distilling Image-Text Matching Model Requirements Python 3.7 Pytorch 1.2 Prepare data Please use git clone --recurse

YE Zhou 60 Dec 16, 2022
RGBD-Net - This repository contains a pytorch lightning implementation for the 3DV 2021 RGBD-Net paper.

[3DV 2021] We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator

Phong Nguyen Ha 4 May 26, 2022
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

37 Dec 15, 2022
A library of multi-agent reinforcement learning components and systems

Mava: a research framework for distributed multi-agent reinforcement learning Table of Contents Overview Getting Started Supported Environments System

InstaDeep Ltd 463 Dec 23, 2022
Get a Grip! - A robotic system for remote clinical environments.

Get a Grip! Within clinical environments, sterilization is an essential procedure for disinfecting surgical and medical instruments. For our engineeri

Jay Sharma 1 Jan 05, 2022
code for paper "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?"

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Code for paper: Does Unsupervised Architecture Representation

39 Dec 17, 2022
B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search

B2EA: An Evolutionary Algorithm Assisted by Two Bayesian Optimization Modules for Neural Architecture Search This is the offical implementation of the

SNU ADSL 0 Feb 07, 2022
Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models

Experimental code for paper: Generative Adversarial Networks as Variational Training of Energy Based Models, under review at ICLR 2017 requirements: T

Shuangfei Zhai 18 Mar 05, 2022
Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
Implementation of ProteinBERT in Pytorch

ProteinBERT - Pytorch (wip) Implementation of ProteinBERT in Pytorch. Original Repository Install $ pip install protein-bert-pytorch Usage import torc

Phil Wang 92 Dec 25, 2022
Source code for CVPR 2020 paper "Learning to Forget for Meta-Learning"

L2F - Learning to Forget for Meta-Learning Sungyong Baik, Seokil Hong, Kyoung Mu Lee Source code for CVPR 2020 paper "Learning to Forget for Meta-Lear

Sungyong Baik 29 May 22, 2022
NeRD: Neural Reflectance Decomposition from Image Collections

NeRD: Neural Reflectance Decomposition from Image Collections Project Page | Video | Paper | Dataset Implementation for NeRD. A novel method which dec

Computergraphics (University of Tübingen) 195 Dec 29, 2022