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Found 4 papers, 1 papers with code
Switching-Aligned-Words Data Augmentation for Neural Machine Translation
In neural machine translation (NMT), data augmentation methods such as back-translation make it possible to use extra monolingual data to help improve translation performance, while it needs extra training data and the in-domain monolingual data is not always available.
Hierarchical Contextualized Representation for Named Entity Recognition
In this paper, we address these two deficiencies and propose a model augmented with hierarchical contextualized representation: sentence-level representation and document-level representation.
Ranked #13 on
Named Entity Recognition (NER)
on Ontonotes v5 (English)
(using extra training data)
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.
Lattice-Based Transformer Encoder for Neural Machine Translation
To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.
