We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a lar...
David Weiss, Chris Alberti, Michael Collins, Slav ...
Measuring word relatedness is an important ingredient of many NLP applications. Several datasets have been developed in order to evaluate such measures. The main drawback of exist...
Ran Levy, Liat Ein-Dor, Shay Hummel, Ruty Rinott, ...
The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics. However, prior work on the text to 3...
Angel X. Chang, Will Monroe, Manolis Savva, Christ...
The performance of discriminative constituent parsing relies crucially on feature engineering, and effective features usually have to be carefully selected through a painful manua...
We present paired learning and inference algorithms for significantly reducing computation and increasing speed of the vector dot products in the classifiers that are at the hea...
Emma Strubell, Luke Vilnis, Kate Silverstein, Andr...