4 citations · 4 across the 2 of their papers we have counts for
6 papers
Low Resource Multi-Task Sequence Tagging -- Revisiting Dynamic Conditional Random Fields
Jonas Pfeiffer, Edwin Simpson, Iryna Gurevych
We compare different models for low resource multi-task sequence tagging that leverage dependencies between label sequences for different tasks. Our analysis is aimed at datasets w…
Scalable Bayesian Preference Learning for Crowds
Edwin Simpson, Iryna Gurevych
We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' o…
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
Edwin Simpson, Yang Gao, Iryna Gurevych
For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs.…
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Edwin Simpson, Steven Reece, Stephen J. Roberts
Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these diff…
Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Steffen Eger, Gözde Gül Şahin, Andreas Rücklé +6
Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"), among other scenarios. W…
A Bayesian Approach for Sequence Tagging with Crowds
Edwin Simpson, Iryna Gurevych
Current methods for sequence tagging, a core task in NLP, are data hungry, which motivates the use of crowdsourcing as a cheap way to obtain labelled data. However, annotators are…