Reliable automatic evaluation of summarization systems is challenging du...
We present Amos, a stochastic gradient-based optimizer designed for trai...
Much of text-to-speech research relies on human evaluation, which incurs...
In this work, we explore whether modeling recurrence into the Transforme...
Recent developments in machine translation and multilingual text generat...
The highly popular Transformer architecture, based on self-attention, is...
We propose a straightforward vocabulary adaptation scheme to extend the
...
The quality of machine translation systems has dramatically improved ove...
Unsupervised translation has reached impressive performance on resource-...
We present ToTTo, an open-domain English table-to-text dataset with over...
Text generation has made significant advances in the last few years. Yet...
We present a probabilistic framework for multilingual neural machine
tra...
We study the problem of model extraction in natural language processing,...
Neural conditional text generation systems have achieved significant pro...
Existing open-domain question answering (QA) models are not suitable for...
We propose a novel conditioned text generation model. It draws inspirati...
Generalization and reliability of multilingual translation often highly
...
The high-dimensional data setting, in which p >> n, is a challenging
sta...
The current trend of extractive question answering (QA) heavily relies o...
Reading comprehension is a challenging task, especially when executed ac...
We propose a simple neural architecture for natural language inference. ...
We present power low rank ensembles (PLRE), a flexible framework for n-g...
Latent variable models are an elegant framework for capturing rich
proba...
Actors in realistic social networks play not one but a number of diverse...