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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Speaker Information Can Guide Models to Better Inductive Biases: A Case Study On Predicting Code-Switching ... Ostapenko, Alissa; Wintner, Shuly; Fricke, Melinda. - : arXiv, 2022 BASE Show details 2 Read, spot and translate Specia, Lucia; Wang, Josiah; Lee, Sun Jae; Ostapenko, Alissa; Madhyastha, Pranava In: Mach Transl (2021) Abstract: We propose multimodal machine translation (MMT) approaches that exploit the correspondences between words and image regions. In contrast to existing work, our referential grounding method considers objects as the visual unit for grounding, rather than whole images or abstract image regions, and performs visual grounding in the source language, rather than at the decoding stage via attention. We explore two referential grounding approaches: (i) implicit grounding, where the model jointly learns how to ground the source language in the visual representation and to translate; and (ii) explicit grounding, where grounding is performed independent of the translation model, and is subsequently used to guide machine translation. We performed experiments on the Multi30K dataset for three language pairs: English–German, English–French and English–Czech. Our referential grounding models outperform existing MMT models according to automatic and human evaluation metrics. Keyword: Article URL: https://doi.org/10.1007/s10590-021-09259-z http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550676/ BASE Hide details

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