Shailza Jolly | DeepAI

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∙ 12/13/2021

Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing

Fact-checking systems have become important tools to verify fake and mis...

0 Shailza Jolly, et al. ∙

∙ 12/06/2021

Search and Learn: Improving Semantic Coverage for Data-to-Text Generation

Data-to-text generation systems aim to generate text descriptions based ...

0 Shailza Jolly, et al. ∙

∙ 02/02/2021

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

We introduce GEM, a living benchmark for natural language Generation (NL...

5 Sebastian Gehrmann, et al. ∙

∙ 10/28/2020

Leveraging Visual Question Answering to Improve Text-to-Image Synthesis

Generating images from textual descriptions has recently attracted a lot...

1 Stanislav Frolov, et al. ∙

∙ 03/26/2020

P ≈ NP, at least in Visual Question Answering

In recent years, progress in the Visual Question Answering (VQA) field h...

19 Shailza Jolly, et al. ∙

∙ 09/12/2018

The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA

We introduce MASSES, a simple evaluation metric for the task of Visual Q...

0 Shailza Jolly, et al. ∙

∙ 08/25/2018

How do Convolutional Neural Networks Learn Design?

In this paper, we aim to understand the design principles in book cover ...

0 Shailza Jolly, et al. ∙

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