Hi All, (apologies for cross-posting, but I imagine that many people on this list are also interested in NMT)
I'd like to note that there will be a shared task on efficient Neural Machine Translation at WNMT2018 (co-located with ACL2018). > https://sites.google.com/site/wnmt18/shared-task If you're interested in making memory or compute efficient NMT models, we'd love to have you participate! More details below. Also, we're still looking for research paper submissions to the workshop, so we'd encourage people to join that as well. Graham ----------------- Basic Idea The basic idea of this task (inspired by the small NMT task at the workshop on Asian Translation) is that for NMT, not only accuracy, but also test-time efficiency is important. Efficiency can include a number of concepts: * Memory Efficiency: We would like to have small models. Evaluation measures include: ** Size on disk of the model ** Number of parameters of the model ** Size in memory of the full program * Computational Efficiency: We would like to have fast models. Evaluation measures include: ** Time to decode the test set in a single CPU thread ** Time to decode the test set on a single GPU Tracks The goal of the task will be to find systems that are both accurate and efficient. In other words, we want to find systems on the Pareto Frontier of efficiency in accuracy. Participants can submit any system that they like, and any system on this Pareto frontier will be considered advantageous. However, we will particularly highlight systems that satisfy one of the two categories: * Efficiency track: We will have a track where the models that perform at least as well as the baseline attempt to create the most efficient implementation. Here, the winner will be the system that achieves a baseline BLEU score with the highest efficiency, memory or computational. * Accuracy track: We will have a track where models that are at least as efficient as the baseline attempt to improve the BLEU score. Here, the winner will be the system that can improve accuracy the most without a decrease in efficiency. _______________________________________________ Moses-support mailing list Moses-support@mit.edu http://mailman.mit.edu/mailman/listinfo/moses-support
