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Pytorch Neural Symbolic Machines

This repository is a Pytorch implementation of Google's neural symbolic machines model (Liang et al., 2018) for weakly-supervised semantic parsing. Specifically, we implement the memory augmented policy optimization (MAPO) algorithm for reinforcement learning of semantic parsers on the WikiTableQuestions environment. This codebase is used in Yin et al. (2020).

Key Difference with the Official TensorFlow Implementation This implementation differs from the official tensorflow implementation in the following ways:

The original model uses GloVe word embeddings and bi-directional LSTM networks as the encoder of utterances and table schemas (table columns). This implementation uses pre-trained BERT/TaBERT models using the table representation strategy proposed in Yin et al. (2020). It significantly outperforms the LSTM-based model, registering ~49.4% denotation-level exact-match accuracies on the test set (the original accuracy is 43.8).
We revised the data pre-processing script, and re-processed the WikiTableQuestions dataset. We also turned off the --anonymize_datetime_and_number_entities option when preprocessing the dataset, which gave better performance.
Besides performance improvement, we also optimized the original codebase for general efficiency improvement. The training speed is ~2 times faster than the original tensorflow implemenation when using an LSTM encoder.

Setup

Install TaBERT The BERT representation learning layer depends on the TaBERT model, which is a pre-trained language model for learning representations of natural language utterances and semi-structured Web tables. Please install TaBERT following the instructions at here.

Conda Environment Next, update the tabert conda environment shipped with TaBERT with dependencies of this project:

conda env update --name tabert --file data/env.yml

Download Dependencies We provide a pre-processed version of the WikiTableQuestions dataset, which could be downloaded from:

wget http://www.cs.cmu.edu/~pengchey/pytorch_nsm.zip
unzip pytorch_nsm.zip    # extract pre-processed dataset under the `data` directory.

As discussed above, the key difference of our version of the pre-processed data and the original ones used in Liang et al. (2018) is that we did not anonymize named entities in questions.

Usage

The following command demos training a MAPO model using BERT (bert-base-uncased) as the representation learning layer:

conda activate tabert

OMP_NUM_THREADS=1 python -m \
  table.experiments \
  train \
  seed 0 \
  --cuda \
  --work-dir=runs/demo_run \
  --config=data/config/config.vanilla_bert.json

Training BERT requires at least two GPUs, one for the learner to perform asynchronous gradient updates, and another one for actors to perform sampling. Since each actor maintains its own copy of BERT, running this model requires large GPU memory. In our experiments we used 8x 16GB V100 GPUs.

To improve GPU memory efficiency, the model also includes aBERT server mode, which allows all actors to share a single BERT model hosted on GPU, while the remaining parameters of actors are hosted on CPU. This significantly reduces memory usage, and the model could be trained on two 1080 GPUs with 11GB memory each. However, since gradient updates of all actors and the centralized BERT model are performed asynchronously, this might result in marginal performance drop. To enable the BERT server mode, simply change the actor_use_table_bert_proxy property to true in the config file:

OMP_NUM_THREADS=1 python -m \
  table.experiments \
  train \
  seed 0 \
  --cuda \
  --work-dir=runs/demo_run \
  --config=data/config/config.vanilla_bert.json \
  --extra-config='{"actor_use_table_bert_proxy": true}'

To perform evaluation with a trained model:

python -m table.experiments.py \
  test \
  --cuda \
  --model runs/demo_run/model.best.bin \
  --test-file data/wikitable_questions/wtq_preprocess_0805_no_anonymize_ent/test_split.json \

Training with pre-trained TaBERT models

To be released.

Acknowledgement

We are grateful to Chen Liang, author of the original MAPO paper, for all the technical discussions.

Reference

Please consider citing the following papers if you are using our codebase.

@inproceedings{pasupat-liang-2015-compositional,
    title = "Compositional Semantic Parsing on Semi-Structured Tables",
    author = "Pasupat, Panupong and Liang, Percy",
    booktitle = "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    year = "2015",
    pages = "1470--1480",
}

@incollection{NIPS2018_8204,
title = {Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing},
author = {Liang, Chen and Norouzi, Mohammad and Berant, Jonathan and Le, Quoc V and Lao, Ni},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {10015--10027},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8204-memory-augmented-policy-optimization-for-program-synthesis-and-semantic-parsing.pdf}
}

@inproceedings{liang2017neural,
  title={Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision},
  author={Liang, Chen and Berant, Jonathan and Le, Quoc and Forbus, Kenneth D and Lao, Ni},
  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  volume={1},
  pages={23--33},
  year={2017}
}

@inproceedings{yin20acl,
    title = {Ta{BERT}: Pretraining for Joint Understanding of Textual and Tabular Data},
    author = {Pengcheng Yin and Graham Neubig and Wen-tau Yih and Sebastian Riedel},
    booktitle = {Annual Conference of the Association for Computational Linguistics (ACL)},
    month = {July},
    year = {2020}
}