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
Pytorch Implementation of rpautrat/SuperPoint

SuperPoint-Pytorch (A Pure Pytorch Implementation) SuperPoint: Self-Supervised Interest Point Detection and Description Thanks This work is based on:

76 Dec 27, 2022
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae In our paper, we p

Rishikesh (ऋषिकेश) 31 Dec 08, 2022
Bayesian Neural Networks in PyTorch

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

Jurijs Nazarovs 7 May 03, 2022
A naive ROS interface for visualDet3D.

YOLO3D ROS Node This repo contains a Monocular 3D detection Ros node. Base on https://github.com/Owen-Liuyuxuan/visualDet3D All parameters are exposed

Yuxuan Liu 19 Oct 08, 2022
SplineConv implementation for Paddle.

SplineConv implementation for Paddle This module implements the SplineConv operators from Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Mül

北海若 3 Dec 29, 2021
AlgoVision - A Framework for Differentiable Algorithms and Algorithmic Supervision

NeurIPS 2021 Paper "Learning with Algorithmic Supervision via Continuous Relaxations"

Felix Petersen 76 Jan 01, 2023
LV-BERT: Exploiting Layer Variety for BERT (Findings of ACL 2021)

LV-BERT Introduction In this repo, we introduce LV-BERT by exploiting layer variety for BERT. For detailed description and experimental results, pleas

Weihao Yu 14 Aug 24, 2022
Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

CLIPstyler Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition" Environment Pytorch 1.7.1, Python 3.6 $ c

201 Dec 29, 2022
Source code for the paper "PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction" in ACL2021

PLOME:Pre-training with Misspelled Knowledge for Chinese Spelling Correction (ACL2021) This repository provides the code and data of the work in ACL20

197 Nov 26, 2022
PAIRED in PyTorch 🔥

PAIRED This codebase provides a PyTorch implementation of Protagonist Antagonist Induced Regret Environment Design (PAIRED), which was first introduce

UCL DARK Lab 46 Dec 12, 2022
Facestar dataset. High quality audio-visual recordings of human conversational speech.

Facestar Dataset Description Existing audio-visual datasets for human speech are either captured in a clean, controlled environment but contain only a

Meta Research 87 Dec 21, 2022
CVPR 2021: "Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE"

Diverse Structure Inpainting ArXiv | Papar | Supplementary Material | BibTex This repository is for the CVPR 2021 paper, "Generating Diverse Structure

152 Nov 04, 2022
Global-Local Context Network for Person Search

Global-Local Context Network for Person Search Abstract: Person search aims to jointly localize and identify a query person from natural, uncropped im

Peng Zheng 15 Oct 17, 2022
The code for replicating the experiments from the LFI in SSMs with Unknown Dynamics paper.

Likelihood-Free Inference in State-Space Models with Unknown Dynamics This package contains the codes required to run the experiments in the paper. Th

Alex Aushev 0 Dec 27, 2021
A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maximum bidding

Business Problem A commany has recently introduced a new type of bidding, the average bidding, as an alternative to the bid given to the current maxim

Kübra Bilinmiş 1 Jan 15, 2022
This is official implementaion of paper "Token Shift Transformer for Video Classification".

This is official implementaion of paper "Token Shift Transformer for Video Classification". We achieve SOTA performance 80.40% on Kinetics-400 val. Paper link

VideoNet 60 Dec 30, 2022
Lane follower: Lane-detector (OpenCV) + Object-detector (YOLO5) + CAN-bus

Lane Follower This code is for the lane follower, including perception and control, as shown below. Environment Hardware Industrial Camera Intel-NUC(1

Siqi Fan 3 Jul 07, 2022
Code for our TKDE paper "Understanding WeChat User Preferences and “Wow” Diffusion"

wechat-wow-analysis Understanding WeChat User Preferences and “Wow” Diffusion. Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang,

18 Sep 16, 2022
Implementation of U-Net and SegNet for building segmentation

Specialized project Created by Katrine Nguyen and Martin Wangen-Eriksen as a part of our specialized project at Norwegian University of Science and Te

Martin.w-e 3 Dec 07, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 07, 2023