We show that on the GLUE tasks, the combination of our pretrained CNN with ALBERT outperforms the original ALBERT and achieves a similar performance to that of SOTA.
Furthermore, we investigate the effect of supplying background knowledge to our classifiers.
In this paper, we propose a method for why-question answering (why-QA) that uses an adversarial learning framework.
We demonstrate our large-scale NLP systems: WISDOM X, DISAANA, and D-SUMM.
Up-propagation is an algorithm for inverting and learning neural network generative models Sensory input is processed by inverting a model that generates patterns from hidden variables using topdown connections The inversion process is iterative utilizing a negative feedback loop that depends on an error signal propagated by bottomup connections The error signal is also used to learn the generative model from examples The algorithm is benchmarked against principal component analysis in experiments on images of handwritten digits.