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        <title>The unreasonable effectiveness of Neural Models in Language Decoding</title>
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            <title>Proceedings of Machine Translation Summit XVII: Tutorial Abstracts</title>
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        <name type="personal">
            <namePart type="given">Laura</namePart>
            <namePart type="family">Rossi</namePart>
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            <publisher>European Association for Machine Translation</publisher>
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                <placeTerm type="text">Dublin, Ireland</placeTerm>
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    <abstract>This tutorial will provide an in-depth look at the experiments, jointly carried out by KantanMT and eBay during 2018, to determine which Neural Model delivers the best translation performance for eBay Customer Service content. It will lay out the timeline, process and mechanisms used to customise Neural MT models and how these were used in conjunction with Human Based evaluations to determine which approach to Neural MT provided the best translation outcomes.The tutorial will cover the following topics and methods:- Structural differences in Neural Networks and how they assist the language decoding process – RNN, CNN and TNN will be covered in detailed.- Customisation of Neural MT using the KantanMT Platform- Using MQM Framework for the evaluation and comparison of Translation Outputs and comparison to Human Translation- Collation and analysis of experimental findings in reaching our decision to standardise on Transformer type networks.Participants of the tutorial will get a clear understanding of Neural Model types and the differences, it will also cover how to customise these models and then how to set up a controlled experiment to determine translation performance.</abstract>
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%0 Conference Proceedings
%T The unreasonable effectiveness of Neural Models in Language Decoding
%A O’Dowd, Tony
%Y Rossi, Laura
%S Proceedings of Machine Translation Summit XVII: Tutorial Abstracts
%D 2019
%8 August
%I European Association for Machine Translation
%C Dublin, Ireland
%F o-dowd-2019-unreasonable
%X This tutorial will provide an in-depth look at the experiments, jointly carried out by KantanMT and eBay during 2018, to determine which Neural Model delivers the best translation performance for eBay Customer Service content. It will lay out the timeline, process and mechanisms used to customise Neural MT models and how these were used in conjunction with Human Based evaluations to determine which approach to Neural MT provided the best translation outcomes.The tutorial will cover the following topics and methods:- Structural differences in Neural Networks and how they assist the language decoding process – RNN, CNN and TNN will be covered in detailed.- Customisation of Neural MT using the KantanMT Platform- Using MQM Framework for the evaluation and comparison of Translation Outputs and comparison to Human Translation- Collation and analysis of experimental findings in reaching our decision to standardise on Transformer type networks.Participants of the tutorial will get a clear understanding of Neural Model types and the differences, it will also cover how to customise these models and then how to set up a controlled experiment to determine translation performance.
%U https://aclanthology.org/W19-7601

Markdown (Informal)

[The unreasonable effectiveness of Neural Models in Language Decoding](https://aclanthology.org/W19-7601) (O'Dowd, MTSummit 2019)

• The unreasonable effectiveness of Neural Models in Language Decoding (O’Dowd, MTSummit 2019)

ACL

• Tony O'Dowd. 2019. The unreasonable effectiveness of Neural Models in Language Decoding. In Proceedings of Machine Translation Summit XVII: Tutorial Abstracts, Dublin, Ireland. European Association for Machine Translation.