Official repository for "PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation"

Related tags

Deep Learningpair-emnlp2020
Overview

pair-emnlp2020

Official repository for the paper:

Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation

If you find our work useful, please cite:

@inproceedings{hua-wang-2020-pair,
    title = "PAIR: Planning and Iterative Refinement in Pre-trained Transformersfor Long Text Generation",
    author = "Hua, Xinyu  and
      Wang, Lu",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}

Requirements

  • Python 3.7
  • PyTorch 1.4.0
  • PyTorchLightning 0.9.0
  • transformers 3.3.0
  • numpy
  • tqdm
  • pycorenlp (for preprocessing nytimes data)
  • nltk (for preprocessing nytimes data)

Data

We release the data sets in the following link(1.2G uncompressed) Please download and uncompress the file, and put under ./data directory. For opinion and news domains, the The New York Times Annotated Corpus is licensed by LDC. We therefore only provide the ids for train/dev/test. Please follow the instructions to generate the dataset.

Text Planning

To train a BERT planner:

cd planning
python train.py \
    --data-path=../data/ \
    --domain=[arggen,opinion,news] \
    --exp-name=demo \
    --save-interval=1 \ # how frequent to save checkpoints 
    --max-epoch=30 \
    --lr=5e-4 \
    --warmup-updates=5000 \
    --train-set=train \
    --valid-set=dev \
    --tensorboard-logdir=tboard/ \
    --predict-keyphrase-offset \
    --max-samples=32 \ # max number of samples per batch
    [--quiet] \ # whether to print intermediate information

The checkpoints will be dumped to checkpoints/planning/[domain]/[exp-name]. Tensorboard will be available under planning/tboard/.

To run inference using a trained model, with greedy decoding:

cd planning
python decode.py \
    --data-path=../data/ \
    --domain=arggen \
    --test-set=test \
    --max-samples=32 \
    --predict-keyphrase-offset \
    --exp-name=demo \
    [--quiet]

The results will be saved to planning/output/.

Iterative Refinement

We provide implementations for four different setups:

  • Seq2seq: prompt -> tgt
  • KPSeq2seq: prompt + kp-set -> tgt
  • PAIR-light: prompt + kp-plan + masks -> tgt
  • PAIR-full: prompt + kp-plan + template -> tgt

To train a model:

cd refinement
python train.py \
    --domain=[arggen,opinion,news] \
    --setup=[seq2seq,kpseq2seq,pair-light,pair-full] \
    --train-set=train \
    --valid-set=dev \
    --train-batch-size=10 \
    --valid-batch-size=5 \
    --num-train-epochs=20 \
    --ckpt-dir=../checkpoints/[domain]/[setup]/demo \
    --tensorboard-dir=demo \
    [--quiet]

To run iterative refinement:

cd refinement
python generate.py \
    --domain=[arggen,opinion,news] \
    --setup=[seq2seq,kpseq2seq,pair-light,pair-full] \
    --test-set=test \
    --output-name=test_demo \
    --enforce-template-strategy=flexible \
    --do-sampling \
    --sampling-topk=100 \
    --sampling-topp=0.9 \
    --sample-times=3 \
    --ckpt-dir=../checkpoints/[domain]/[setup]/demo

Contact

Xinyu Hua (hua.x [at] northeastern.edu)

License

See the LICENSE file for details.

Owner
Xinyu Hua
PhD student at Northeastern University
Xinyu Hua
GitHub Repository
Adaptive Graph Convolution for Point Cloud Analysis

Adaptive Graph Convolution for Point Cloud Analysis This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph

64 Dec 21, 2022
Deep Video Matting via Spatio-Temporal Alignment and Aggregation [CVPR2021]

Deep Video Matting via Spatio-Temporal Alignment and Aggregation [CVPR2021] Paper: https://arxiv.org/abs/2104.11208 Introduction Despite the significa

76 Dec 07, 2022
Multi-Task Deep Neural Networks for Natural Language Understanding

New Release We released Adversarial training for both LM pre-training/finetuning and f-divergence. Large-scale Adversarial training for LMs: ALUM code

Xiaodong 2.1k Dec 30, 2022
NeuralForecast is a Python library for time series forecasting with deep learning models

NeuralForecast is a Python library for time series forecasting with deep learning models. It includes benchmark datasets, data-loading utilities, evaluation functions, statistical tests, univariate m

Nixtla 1.1k Jan 03, 2023
R-package accompanying the paper "Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction"

dffm The goal of dffm is to provide functionality to apply the methods developed in the paper “Dynamic Factor Model for Functional Time Series: Identi

Sven Otto 3 Dec 09, 2022
QKeras: a quantization deep learning library for Tensorflow Keras

QKeras github.com/google/qkeras QKeras 0.8 highlights: Automatic quantization using QKeras; Stochastic behavior (including stochastic rouding) is disa

Google 437 Jan 03, 2023
ICLR 2021 i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning

Introduction PyTorch code for the ICLR 2021 paper [i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning]. @inproceedings{lee2021i

Kibok Lee 68 Nov 27, 2022
Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

custom-cnn-fashion-mnist Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow. The following

Danielle Almeida 1 Mar 05, 2022
Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*

Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*. The algorithm was extremely

1 Mar 28, 2022
CasualHealthcare's Pneumonia detection with Artificial Intelligence (Convolutional Neural Network)

CasualHealthcare's Pneumonia detection with Artificial Intelligence (Convolutional Neural Network) This is PneumoniaDiagnose, an artificially intellig

Azhaan 2 Jan 03, 2022
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference

RawVSR This repo contains the official codes for our paper: Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference Xiaoh

Xiaohong Liu 23 Oct 08, 2022
Deep Learning segmentation suite designed for 2D microscopy image segmentation

Deep Learning segmentation suite dessigned for 2D microscopy image segmentation This repository provides researchers with a code to try different enco

7 Nov 03, 2022
:hot_pepper: R²SQL: "Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing." (AAAI 2021)

R²SQL The PyTorch implementation of paper Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing. (AAAI 2021) Requirement

huybery 60 Dec 31, 2022
Open source annotation tool for machine learning practitioners.

doccano doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequ

7.1k Jan 01, 2023
This repo is the official implementation of "L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization".

L2ight is a closed-loop ONN on-chip learning framework to enable scalable ONN mapping and efficient in-situ learning. L2ight adopts a three-stage learning flow that first calibrates the complicated p

Jiaqi Gu 9 Jul 14, 2022
DiSECt: Differentiable Simulator for Robotic Cutting

DiSECt: Differentiable Simulator for Robotic Cutting Website | Paper | Dataset | Video | Blog post DiSECt is a simulator for the cutting of deformable

NVIDIA Research Projects 73 Oct 29, 2022
We utilize deep reinforcement learning to obtain favorable trajectories for visual-inertial system calibration.

Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning Update: The lastest code will be updated in this branch. Pleas

ETHZ ASL 27 Dec 29, 2022
Awesome Graph Classification - A collection of important graph embedding, classification and representation learning papers with implementations.

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers

Benedek Rozemberczki 4.5k Jan 01, 2023
JudeasRx - graphical app for doing personalized causal medicine using the methods invented by Judea Pearl et al.

JudeasRX Instructions Read the references given in the Theory and Notation section below Fire up the Jupyter Notebook judeas-rx.ipynb The notebook dra

Robert R. Tucci 19 Nov 07, 2022
TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation

TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmentation Zhaoyun Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li

DamoCV 25 Dec 16, 2022