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- W3167690611 abstract "Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist. We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data. From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence. We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation." @default.
- W3167690611 created "2021-06-22" @default.
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- W3167690611 date "2021-01-01" @default.
- W3167690611 modified "2023-09-30" @default.
- W3167690611 title "Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model" @default.
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- W3167690611 doi "https://doi.org/10.18653/v1/2021.naacl-main.461" @default.
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