We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences.
Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space.
While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018).
Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-art performance in a variety of translation tasks, how to use document-level context to deal with discourse phenomena problematic for Transformer still remains a challenge.
In natural language processing, a lot of the tasks are successfully solved with recurrent neural networks, but such models have a huge number of parameters.
Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features.
In this study, we explore capsule networks with dynamic routing for text classification.
#3 best model for Sentiment Analysis on MR
Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates.