52 citations · 65 across the 8 of their papers we have counts for
8 papers
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran +74
Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using…
Generating Fluent Fact Checking Explanations with Unsupervised Post-Editing
Shailza Jolly, Pepa Atanasova, Isabelle Augenstein
Fact-checking systems have become important tools to verify fake and misguiding news. These systems become more trustworthy when human-readable explanations accompany the veracity…
Search and Learn: Improving Semantic Coverage for Data-to-Text Generation
Shailza Jolly, Zi Xuan Zhang, Andreas Dengel +1
Data-to-text generation systems aim to generate text descriptions based on input data (often represented in the tabular form). A typical system uses huge training samples for learn…
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann, Tosin Adewumi, Karmanya Aggarwal +53
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of auto…
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis
Stanislav Frolov, Shailza Jolly, Jörn Hees +1
Generating images from textual descriptions has recently attracted a lot of interest. While current models can generate photo-realistic images of individual objects such as birds a…
P NP, at least in Visual Question Answering
Shailza Jolly, Sebastian Palacio, Joachim Folz +3
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets. One of the most widely-used of these is the…
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA
Shailza Jolly, Sandro Pezzelle, Tassilo Klein +2
We introduce MASSES, a simple evaluation metric for the task of Visual Question Answering (VQA). In its standard form, the VQA task is operationalized as follows: Given an image an…
How do Convolutional Neural Networks Learn Design?
Shailza Jolly, Brian Kenji Iwana, Ryohei Kuroki +1
In this paper, we aim to understand the design principles in book cover images which are carefully crafted by experts. Book covers are designed in a unique way, specific to genres…