Context in Neural Machine Translation: A Review of Models and Evaluations
arXiv:1901.09115
Abstract
This review paper discusses how context has been used in neural machine translation (NMT) in the past two years (2017-2018). Starting with a brief retrospect on the rapid evolution of NMT models, the paper then reviews studies that evaluate NMT output from various perspectives, with emphasis on those analyzing limitations of the translation of contextual phenomena. In a subsequent version, the paper will then present the main methods that were proposed to leverage context for improving translation quality, and distinguishes methods that aim to improve the translation of specific phenomena from those that consider a wider unstructured context.
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- A Neural Entity Coreference Resolution Review
- A Survey on Document-level Neural Machine Translation: Methods and Evaluation
- Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models
- Improving Conditioning in Context-Aware Sequence to Sequence Models
- Fill in the Blanks: Imputing Missing Sentences for Larger-Context Neural Machine Translation
- Neural Machine Translation: A Review and Survey