52 citations · 56 across the 3 of their papers we have counts for
3 papers
Does Putting a Linguist in the Loop Improve NLU Data Collection?
Alicia Parrish, William Huang, Omar Agha +7
Many crowdsourced NLP datasets contain systematic gaps and biases that are identified only after data collection is complete. Identifying these issues from early data samples durin…
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…
Trawling for Trolling: A Dataset
Hitkul, Karmanya Aggarwal, Pakhi Bamdev +3
The ability to accurately detect and filter offensive content automatically is important to ensure a rich and diverse digital discourse. Trolling is a type of hurtful or offensive…