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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2566/MS-AMLV-2019-invited2.pdf</pdf>
<pre>
               Fitting Machine Translation into Clients
                                        (Keynote talk)


                                      Kenneth Heafield

         Institute for Language, Cognition and Computation, University of Edinburgh,
         IF 4.21, 10 Crichton Street, Edinburgh, EH8 9AB, Scotland, European Union

                                       kheafiel@inf.ed.ac.uk



        Abstract. The Bergamot project is making neural machine translation efficient
        enough to run with high quality on a desktop, preserving privacy compared to
        online services. Doing so requires us to compress the model to fit in reasonable
        memory and run fast on a wide range of CPUs.


        Keywords: neural machine translation, efficiency, privacy preservation, model
        compression.




                This project has received funding from the European Union’s Horizon 2020
                research and innovation programme under grant agreement No 825303.




Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
In: Proceedings of the 1st Masters Symposium on Advances in Data Mining, Machine Learning,
and Computer Vision (MS-AMLV 2019), Lviv, Ukraine, November 15-16, 2019, p. 1

</pre>