( Image credit: SQuAD )
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To demonstrate this ambiguity, we construct a modality selector (or disambiguator) network, and this model gets substantially lower accuracy on our challenge set, compared to existing datasets, indicating that our questions are more ambiguous.
There is a perennial need in the online advertising industry to refresh ad creatives, i. e., images and text used for enticing online users towards a brand.
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding.
Understanding stories is a challenging reading comprehension problem for machines as it requires reading a large volume of text and following long-range dependencies.
In particular, we propose a tree-based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.
Such research will require data and tools, to allow the implementation and study of conversational systems.
In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation.
We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model.
Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly.