Conference paper Open Access

Lexical Micro-adaptation for Neural Machine Translation

Xu, Jitao; Crego, Josep; Senellart, Jean


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>This work is inspired by a typical machine translation industry scenario in which translators make use of in-domain data for facilitating translation of similar or repeating sentences. We introduce a generic framework applied at inference in which a subset of segment pairs are first extracted from training data according to their similarity to the input sentences. These segments are then used to dynamically update the parameters of a generic NMT network, thus performing a&nbsp;lexical micro-adaptation. Our approach demonstrates strong adaptation performance to new and existing datasets including pseudo in-domain data. We evaluate our approach on a heterogeneous English-French training dataset showing accuracy gains on all evaluated domains when compared to strong adaptation baselines.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "SYSTRAN, 5 rue Feydeau, 75002 Paris (France)", 
      "@type": "Person", 
      "name": "Xu, Jitao"
    }, 
    {
      "affiliation": "SYSTRAN, 5 rue Feydeau, 75002 Paris (France)", 
      "@type": "Person", 
      "name": "Crego, Josep"
    }, 
    {
      "affiliation": "SYSTRAN, 5 rue Feydeau, 75002 Paris (France)", 
      "@type": "Person", 
      "name": "Senellart, Jean"
    }
  ], 
  "headline": "Lexical Micro-adaptation for Neural Machine Translation", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-11-02", 
  "url": "https://zenodo.org/record/3524977", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3524977", 
  "@id": "https://doi.org/10.5281/zenodo.3524977", 
  "@type": "ScholarlyArticle", 
  "name": "Lexical Micro-adaptation for Neural Machine Translation"
}
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