08/12/2020

A Neural Model for Aggregating Coreference Annotation in Crowdsourcing

Maolin Li, Hiroya Takamura, Sophia Ananiadou

Keywords:

Abstract Paper Similar Papers

Abstract: Coreference resolution is the task of identifying all mentions in a text that refer to the same real-world entity. Collecting sufficient labelled data from expert annotators to train a high-performance coreference resolution system is time-consuming and expensive. Crowdsourcing makes it possible to obtain the required amounts of data rapidly and cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this paper, we split the aggregation into two subtasks, i.e, mention classification and coreference chain inference. Firstly, we predict the general class of each mention using an autoencoder, which incorporates contextual information about each mention, while at the same time taking into account the mention’s annotation complexity and annotators’ reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into account the learned reliability in the first subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We also illustrate our model’s interpretability through a comprehensive analysis of experimental results.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6196-a-neural-model-for-aggregating-coreference-annotation-in-crowdsourcing
(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 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
 9:33 
 0
 0
 0
 0
 6:58 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 4:45 
 0
 0
 0
 0
 14:29 
 0
 0
 0
 0
 5:25 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 8:11 
 0
 0
 0
 0
 11:54 
 0
 0
 0
 0
 12:16 
 0
 0
 0
 0
 16:40 
 0
 0
 0
 0
 11:39 
 0
 0
 0
 0
 10:55 
 0
 0
 0
 0
 9:46 
 0
 0
 0
 0
 9:47 
 0
 0
 0
 0
 4:59 
 0
 0
 0
 0
 11:55 
 0
 0
 0
 0
 13:58 
 0
 0
 0
 0
 12:47 
 0
 0
 0
 0
 14:36 
 0
 0
 0
 0
 9:51 
 0
 0
 0
 0
 16:26 
 0
 0
 0
 0
 11:48 
 0
 0
 0
 0
 4:58 
 0
 0
 0
 0
 14:30 
 0
 0
 0
 0
 12:16 
 0
 0
 0
 0
 12:33 
 0
 0
 0
 0
 10:19 
 0
 0
 0
 0
 9:29 
 0
 0
 0
 0
 20:31 
 0
 0
 0
 0
 16:10 
 0
 0
 0
 0
 13:17 
 0
 0
 0
 0
 2:53 
 0
 0
 0
 0
 6:53 
 0
 0
 0
 0
 10:03 
 0
 0
 0
 0
 10:14 
 0
 0
 0
 0
 7:08 
 0
 0
 0
 0
 5:30 
 0
 0
 0
 0
 9:57 
 0
 0
 0
 0
 14:38