Стохастический градиентный спуск

We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a dis- criminative, convex, semi-supervised learning …...

Sequence labeling models like conditional random fields have been successfully applied in a variety of NLP tasks. However, as the size of label set and dataset grows, the learning speed …...

We present an online learning algorithm for statistical machine translation (SMT) based on stochastic gradient descent (SGD). Under the online setting of rank learning, a corpus-wise loss has to be …...

AbstractDue to the nature of complex NLP problems, structured prediction algorithms have been important modeling tools for a wide range of tasks. While there exists evidence showing that linear Structural …...

We present a fast and scalable online method for tuning statistical machine trans- lation models with large feature sets. The standard tuning algorithm—MERT—only scales to tens of features. Recent discrimi- …...

This work presents a flexible and efficient discriminative training approach for statisti- cal machine translation. We propose to use the RPROP algorithm for optimizing a max- imum expected BLEU objective …...

This paper addresses the problem of comment classification in community Question Answer- ing. Following the state of the art, we ap- proach the task with a global inference pro- cess …...

AbstractOne way to reduce network traffic in multi-node data-parallel stochastic gradient descent is to only exchange the largest gradients. However, doing so damages the gradient and degrades the model’s performance. …...

AbstractIn this paper, we present the system submitted to “SemEval-2020 Task 12”. The proposed system aims at automatically identify the Offensive Language in Arabic Tweets. A machine learning based approach …...