08/12/2020

Bayesian Methods for Semi-supervised Text Annotation

Kristian Miok, Gregor Pirs, Marko Robnik-Sikonja

Keywords:

Abstract Paper Similar Papers

Abstract: Human annotations are an important source of information in the development of natural language understanding approaches. As under the pressure of productivity annotators can assign different labels to a given text, the quality of produced annotations frequently varies. This is especially the case if decisions are difficult, with high cognitive load, requires awareness of broader context, or careful consideration of background knowledge. To alleviate the problem, we propose two semi-supervised methods to guide the annotation process: a Bayesian deep learning model and a Bayesian ensemble method. Using a Bayesian deep learning method, we can discover annotations that cannot be trusted and might require reannotation. A recently proposed Bayesian ensemble method helps us to combine the annotators’ labels with predictions of trained models. According to the results obtained from three hate speech detection experiments, the proposed Bayesian methods can improve the annotations and prediction performance of BERT models.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6539-bayesian-methods-for-semi-supervised-text-annotation
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at COLING Workshops 2020 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 13:17 
 0
 0
 0
 0
 11:30 
 0
 0
 0
 0
 14:06 
 0
 0
 0
 0
 12:23 
 0
 0
 0
 0
 11:27 
 0
 0
 0
 0
 11:41 
 0
 0
 0
 0
 9:29 
 0
 0
 0
 0
 16:44 
 0
 0
 0
 0
 3:17 
 0
 0
 0
 0
 16:40 
 0
 0
 0
 0
 10:33 
 0
 0
 0
 0
 14:56 
 0
 0
 0
 0
 11:47 
 0
 0
 0
 0
 11:43 
 0
 0
 0
 0
 10:53 
 0
 0
 0
 0
 5:21 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 11:40 
 0
 0
 0
 0
 14:09 
 0
 0
 0
 0
 9:50 
 0
 0
 0
 0
 19:21 
 0
 0
 0
 0
 4:49 
 0
 0
 0
 0
 11:09 
 0
 0
 0
 0
 14:45 
 0
 0
 0
 0
 10:55 
 0
 0
 0
 0
 16:43 
 0
 0
 0
 0
 12:23 
 0
 0
 0
 0
 17:06 
 0
 0
 0
 0
 12:39 
 0
 0
 0
 0
 11:19 
 0
 0
 0
 0
 10:07 
 0
 0
 0
 0
 6:58 
 0
 0
 0
 0
 14:48 
 0
 0
 0
 0
 11:06 
 0
 0
 0
 0
 17:37 
 0
 0
 0
 0
 12:03 
 0
 0
 0
 0
 16:04 
 0
 0
 0
 0
 10:42 
 0
 0
 0
 0
 4:44 
 0
 0
 0
 0
 4:57