Fine-grained Human Evaluation of Transformer and Recurrent Approaches to Neural Machine Translation for English-to-Chinese

Yuying Ye, Antonio Toral

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    Abstract

    This research presents a fine-grained human evaluation to compare the Transformer and recurrent approaches to neural machine translation (MT), on the translation direction English-to-Chinese. To this end, we develop an error taxonomy compliant with the Multidimensional Quality Metrics (MQM) framework that is customised to the relevant phenomena of this translation direction. We then conduct an error annotation using this customised error taxonomy on the output of state-of-the-art recurrent- and Transformer-based MT systems on a subset of WMT2019's news test set. The resulting annotation shows that, compared to the best recurrent system, the best Transformer system results in a 31% reduction of the total number of errors and it produced significantly less errors in 10 out of 22 error categories. We also note that two of the systems evaluated do not produce any error for a category that was relevant for this translation direction prior to the advent of NMT systems: Chinese classifiers.
    Original languageEnglish
    Title of host publicationProceedings of the 22nd Annual Conference of the European Association for Machine Translation
    Place of PublicationLisboa, Portugal
    PublisherEuropean Association for Machine Translation
    Pages125-134
    Number of pages10
    Publication statusPublished - 2020
    EventAnnual Conference of the European Association for Machine Translation - Online, Portugal
    Duration: 3-Nov-20205-Nov-2020
    Conference number: 22

    Conference

    ConferenceAnnual Conference of the European Association for Machine Translation
    Country/TerritoryPortugal
    Period03/11/202005/11/2020

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