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Found 3 papers, 1 papers with code
Controllable Dual Skew Divergence Loss for Neural Machine Translation
Our experiments indicate that switching to the DSD loss after the convergence of ML training helps models escape local optima and stimulates stable performance improvements.
Multilingual Universal Dependency Parsing from Raw Text with Low-Resource Language Enhancement
This paper describes the system of our team Phoenix for participating CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
Finding Better Subword Segmentation for Neural Machine Translation
For different language pairs, word-level neural machine translation (NMT) models with a fixed-size vocabulary suffer from the same problem of representing out-of-vocabulary (OOV) words.
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