Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models.
#3 best model for Dependency Parsing on Penn Treebank
Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem.
In this paper, we propose LexVec, a new method for generating distributed word representations that uses low-rank, weighted factorization of the Positive Point-wise Mutual Information matrix via stochastic gradient descent, employing a weighting scheme that assigns heavier penalties for errors on frequent co-occurrences while still accounting for negative co-occurrence.
Text from social media provides a set of challenges that can cause traditional NLP approaches to fail.
Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP.
#3 best model for Question Answering on CNN / Daily Mail
Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test.
The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation.
#3 best model for Machine Translation on WMT2015 English-German