Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

Introduction

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization.

This project is a Tensorflow implementation of our work.

Requirements

• Python 3.6
• Numpy
• Tensorflow 1.4.0

Quick Start

• Dataset

  Our dataset contains 119K pairs of product specifications and the corresponding advertising text. For more information, please refer to our paper.

• Preprocess

  • Download data from https://drive.google.com/open?id=1vB0fT1ex2Tsid-i5s-jqdz9QUFbCh0CO and unzip the file, which will create a new directory named data. The path to our dataset is ./data/data.jsonl.
  • We provided most preprocessed data under ./data/processed/ except pre-trained word embeddings which can be generated with the following command line:

  bash preprocess.sh
  

• Train

  ./run.sh
  

• Test

  ./test.sh
  

Citation

Our paper is available at https://arxiv.org/abs/1908.06605v2.

Please kindly cite our paper if this paper and the code are helpful.