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.
- Python 3.6
- Numpy
- Tensorflow 1.4.0
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Dataset
Our dataset contains 119K pairs of product specifications and the corresponding advertising text. For more information, please refer to our paper.
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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 - Download data from https://drive.google.com/open?id=1vB0fT1ex2Tsid-i5s-jqdz9QUFbCh0CO and unzip the file, which will create a new directory named
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Train
./run.sh -
Test
./test.sh
Our paper is available at https://arxiv.org/abs/1908.06605v2.
Please kindly cite our paper if this paper and the code are helpful.