2013. Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation. In Proceedings of the 51st Annual Meeting of the Association�...
Abstract. Data selection is an effective approach to domain adaptation in statistical ma- chine translation. The idea is to use lan- guage models trained on�...
It is found that neural language models are indeed viable tools for data selection: while the improvements are varied, they are fast to train on small�...
Adaptation Data Selection using Neural Language Models
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Request PDF | On Aug 1, 2013, Kevin Duh and others published Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation�...
Data selection is an effective approach to domain adaptation in statistical machine translation. The idea is to use language models trained on small in-domain�...
In this work, we introduce a two-stage training framework for NMT where we fine-tune a base NMT model on subsets of data, selected by both deterministic scoring.
Sep 9, 2021 � We propose a cross-lingual data selection method to extract in-domain sentences in the missing language side from a large generic monolingual corpus.
Intelligent selection of training data has proven a successful technique to simul- taneously increase training efficiency and translation performance for�...
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation � Computer Science, Linguistics. ACL � 2013.
Sep 1, 2022 � Domain Adaptation (DA) has been a well-known transfer learning algorithm used in Neural Machine Translation (NMT) task.