This repository contains source code for the ΝΑACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression" (Paper).

Introduction

The paper presents a sequence-to-sequence-to-sequence (SEQ³) autoencoder consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We employ continuous approximations to sampling from categorical distributions, in order to generate the latent sequence of words. This enables gradient-based optimization.

We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input.

Architecture

Reference

@inproceedings{baziotis2019naacl,
    title = {\textsc{SEQ}\textsuperscript{3}: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression},
    author = {Christos Baziotis and Ion Androutsopoulos and Ioannis Konstas and Alexandros Potamianos},
    booktitle = {Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL:HLT)},
    address = {Minneapolis, USA},
    month = {June},
    url = {https://arxiv.org/abs/1904.03651},
    year = {2019}
}

Prerequisites

Dependencies

• PyTorch version >= 1.0.0
• Python version >= 3.6

Install Requirements

Create Environment (Optional): Ideally, you should create an environment for the project.

conda create -n seq3 python=3
conda activate seq3

Install PyTorch 1.0 with the desired Cuda version if you want to use the GPU and then the rest of the requirements:

pip install -r requirements.txt

Download Data

To train the model you need to download the training data and the pretrained word embeddings.

Dataset: In our experiments we used the Gigaword dataset, which can be downloaded from: https://github.com/harvardnlp/sent-summary Extract the data in datasets/gigaword/ and organize the files as:

datasets
└── gigaword
      └── dev/
      └── test1951/
      └── train.article.txt
      └── train.title.txt
      └── valid.article.filter.txt
      └── valid.title.filter.txt

Included in datasets/gigaword/dev/ you will find a small subset of the source (the target summaries are never used) training data, i.e., the articles, which were used for prototyping, as well as a dev set with 4K parallel sentences for evaluation.

You can also use your own data, as long as the source and target data are text files with one sentence per line.

Embeddings: In our experiments we used the "Wikipedia 2014 + Gigaword 5" (6B) Glove embeddings: http://nlp.stanford.edu/data/wordvecs/glove.6B.zip Put the embedding files in the embeddings/ directory.

Training

In order to train a model, either the LM or SEQ³, you need to run the corresponding python script and pass as an argument a yaml model config. The yaml config specifies everything regarding the experiment to be executed. Therefore, if you want to make any changes to a model, change or create a new yaml config. The model config files are under the model_configs/ directory. Use the provided configs as reference. Each parameter is documented in comments, although most of them are self-explanatory.

Train the Language Model prior

In our experiments we trained the LM on the source (only) sentences of the Gigaword dataset.

python models/sent_lm.py --config model_configs/camera/lm_prior.yaml 

After the training ends, the checkpoint with the best validation loss will be saved under the directory checkpoints/.

Train SEQ³

Training the LM prior is a prerequisite for training SEQ³. While you will still be able to train the model without it, the LM prior loss will be disabled.

python models/seq3.py --config model_configs/camera/seq3.full.yaml 

Prototyping: You can experiment with SEQ³ without downloading the full training data, by training with the configs model_configs/lm.yaml and model_configs/seq3.yaml, respectively, which use the small subset of the training data.

Troubleshooting

• If you get the error ModuleNotFoundError: No module named 'X', then add the directory X to your PYTHONPATH in your ~/.bashrc, or simply:

  export PYTHONPATH='.'