Hyperparameter Tuning

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vec- tors for sentence-level classification tasks. We show that a simple CNN …...

This paper aims to compare different reg- ularization strategies to address a com- mon phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neu- ral …...

Distributional semantic models can pre- dict many linguistic phenomena, including word similarity, lexical ambiguity, and se- mantic priming, or even to pass TOEFL synonymy and analogy tests (Landauer and Dumais, …...

AbstractOur goal is to evaluate the usefulness of unsupervised representation learning techniques for detecting stances of Fake News. Therefore we examine several pre-trained language models with respect to their performance …...

AbstractWe analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing F1 score on the Wall Street Journal (WSJ) development set (1,700 sentences). We introduce …...

AbstractWhile large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one …...