Simultaneous NMT/MMT framework in PyTorch

Related tags

Deep Learning machine-translation pytorch sequence-to-sequence simultaneous-translations
Overview

pysimt

License: MIT Python 3.7 Python 3.8 Documentation coverage

This repository includes the codes, the experiment configurations and the scripts to prepare/download data for the Simultaneous Machine Translation with Visual Context paper presented at EMNLP 2020.

Note for RL-based codebase

Please visit the sim-mt repository for the implementation of our RL-based pipeline. Specifically, sim-mt provides codebase for the following papers:

Overview

pysimt is a PyTorch-based sequence-to-sequence framework that facilitates research in unimodal and multi-modal machine translation. The framework is especially geared towards a set of recent simultaneous MT approaches, including heuristics-based decoding and prefix-to-prefix training/decoding. Common metrics such as average proportion (AP), average lag (AL), and consecutive wait (CW) are provided through well-defined APIs as well.

Please visit https://imperialnlp.github.io/pysimt for detailed documentation.

Citation

@inproceedings{caglayan-etal-2020-simultaneous,
    title = "Simultaneous Machine Translation with Visual Context",
    author = {Caglayan, Ozan  and
      Ive, Julia  and
      Haralampieva, Veneta  and
      Madhyastha, Pranava  and
      Barrault, Lo{\"\i}c  and
      Specia, Lucia},
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.184",
    pages = "2350--2361",
}

Installation

The essential dependency of pysimt is torch>=1.7. The following command will create an appropriate Anaconda environment with pysimt installed within in editable mode.

conda env create -f environment.yml

Once the installation is done, run pysimt-install-extra command if you want to use METEOR as an evaluation metric.

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Comments
  • Confuse about model SimultaneousTFWaitKNMT

    Confuse about model SimultaneousTFWaitKNMT

    Class SimultaneousTFWaitKNMT

    def forward(self, batch, **kwargs):
        """
        Performs a forward pass.
        :param batch: The batch.
        :param kwargs: Any extra arguments.
        :return: The output from the forward pass.
        """
        k = int(self.opts.model['translator_args']['k'])
        if self.training:
            epoch_count = kwargs['ectr']
            if epoch_count <= self.opts.model['consecutive_warmup']:
                # warming up, use full contexts
                k = int(1e4)

        # Pass 'k' to the model.
        return super().forward(batch, k=k)

    In the forward process of SimultaneousTFWaitKNMT, encoders uses full context rather than 'first k token' which may cause feature leakage. I think transformer encoders should recalculate encoder hidden state in every encode step. Although k is passed to super class, but not used

    opened by xinshoulzc 2
  • Sample code showing how to use models in pysimt

    Sample code showing how to use models in pysimt

    First of all, thanks alot for such a nice repository. It will definitely be very useful for many people!

    I was wondering if there is an example showing how the models implemented so far can be used? For instance, as far as I see in [1], pysimt supports using wait-k model. However, I didn't quite understand how can I train this wait-k model and use the trained model to perform decoding. Would that be possible for you to share a sample code for such purpose?

    [1] https://github.com/ImperialNLP/pysimt/blob/master/pysimt/models/snmt_rnn_waitk.py

    opened by kurtisxx 0
Owner
NLP@Imperial
Natural Language Processing at Imperial College London
NLP@Imperial
GitHub https://imperialnlp.github.io/pysimt/
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