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Found 5 papers, 1 papers with code
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation
In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation.
NJU’s submission to the WMT20 QE Shared Task
This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20.
Simple and Scalable Nearest Neighbor Machine Translation
$k$NN-MT is a straightforward yet powerful approach for fast domain adaptation, which directly plugs pre-trained neural machine translation (NMT) models with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
Fast and Accurate Neural Machine Translation with Translation Memory
It is generally believed that a translation memory (TM) should be beneficial for machine translation tasks.
DirectQE: Direct Pretraining for Machine Translation Quality Estimation
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
