Conference paper Open Access
Xu, Jitao; Crego, Josep; Senellart, Jean
{ "publisher": "Zenodo", "DOI": "10.5281/zenodo.3524977", "language": "eng", "title": "Lexical Micro-adaptation for Neural Machine Translation", "issued": { "date-parts": [ [ 2019, 11, 2 ] ] }, "abstract": "<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 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>", "author": [ { "family": "Xu, Jitao" }, { "family": "Crego, Josep" }, { "family": "Senellart, Jean" } ], "type": "paper-conference", "id": "3524977" }
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