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Ranking Creative Language Characteristics in Small Data Scenarios
The ability to rank creative natural language provides an important gene...
10/23/2020 ∙ by Julia Siekiera, et al. ∙
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OFAI-UKP at HAHA@IberLEF2019: Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning
Most humour processing systems to date make at best discrete, coarse-gra...
08/03/2020 ∙ by Tristan Miller, et al. ∙
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Low Resource Multi-Task Sequence Tagging – Revisiting Dynamic Conditional Random Fields
We compare different models for low resource multi-task sequence tagging...
05/01/2020 ∙ by Jonas Pfeiffer, et al. ∙
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Generating Persona-Consistent Dialogue Responses Using Deep Reinforcement Learning
Recent transformer-based open-domain dialogue agents are trained by refe...
04/30/2020 ∙ by Mohsen Mesgar, et al. ∙
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Scalable Bayesian Preference Learning for Crowds
We propose a scalable Bayesian preference learning method for jointly pr...
12/04/2019 ∙ by Edwin Simpson, et al. ∙
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Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Unstructured data from diverse sources, such as social media and aerial ...
04/05/2019 ∙ by Edwin Simpson, et al. ∙
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Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems
Visual modifications to text are often used to obfuscate offensive comme...
03/27/2019 ∙ by Steffen Eger, et al. ∙
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Bayesian Ensembles of Crowds and Deep Learners for Sequence Tagging
Current methods for sequence tagging, a core task in NLP, are data hungr...
11/02/2018 ∙ by Edwin Simpson, et al. ∙
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Finding Convincing Arguments Using Scalable Bayesian Preference Learning
We introduce a scalable Bayesian preference learning method for identify...
06/06/2018 ∙ by Edwin Simpson, et al. ∙
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