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Found 6 papers, 1 papers with code
Neural Combinatory Constituency Parsing
We propose two fast neural combinatory models for constituency parsing: binary and multi-branching.
Simpson's Bias in NLP Training
In most machine learning tasks, we evaluate a model $M$ on a given data population $S$ by measuring a population-level metric $F(S;M)$.
Chinese-Japanese Unsupervised Neural Machine Translation Using Sub-character Level Information
Unsupervised neural machine translation (UNMT) requires only monolingual data of similar language pairs during training and can produce bi-directional translation models with relatively good performance on alphabetic languages (Lample et al., 2018).
Neural Machine Translation of Logographic Language Using Sub-character Level Information
Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units.
Neural Machine Translation of Logographic Languages Using Sub-character Level Information
Recent neural machine translation (NMT) systems have been greatly improved by encoder-decoder models with attention mechanisms and sub-word units.
