Recent success of deep learning can be largely attributed to the huge amount of data used for training deep neural networks. However, the sheer amount of data significantly increase the burden on storage and transmission. It would also consume considerable time and computational resources to train...

The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, training parameter-hungry models on large datasets poses multi-faceted problems such as (a) high model-training time; (b) slow...

Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training...