08/12/2020

An analysis of language models for metaphor recognition

Arthur Neidlein, Philip Wiesenbach, Katja Markert

Keywords:

Abstract Paper Similar Papers

Abstract: We conduct a linguistic analysis of recent metaphor recognition systems, all of which are based on language models. We show that their performance, although reaching high F-scores, has considerable gaps from a linguistic perspective. First, they perform substantially worse on unconventional metaphors than on conventional ones. Second, they struggle with handling rarer word types. These two findings together suggest that a large part of the systems’ success is due to optimising the disambiguation of conventionalised, metaphoric word senses for specific words instead of modelling general properties of metaphors. As a positive result, the systems show increasing capabilities to recognise metaphoric readings of unseen words if synonyms or morphological variations of these words have been seen before, leading to enhanced generalisation beyond word sense disambiguation.

The video of this talk cannot be embedded. You can watch it here:
https://underline.io/lecture/6286-an-analysis-of-language-models-for-metaphor-recognition
(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
 5:07 
 0
 0
 0
 0
 18:55 
 0
 0
 0
 0
 9:49 
 0
 0
 0
 0
 11:23 
 0
 0
 0
 0
 11:18 
 0
 0
 0
 0
 12:01 
 0
 0
 0
 0
 11:43 
 0
 0
 0
 0
 11:19 
 0
 0
 0
 0
 11:35 
 0
 0
 0
 0
 12:17 
 0
 0
 0
 0
 12:03 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 1:00 
 0
 0
 0
 0
 14:09 
 0
 0
 0
 0
 6:59 
 0
 0
 0
 0
 14:12 
 0
 0
 0
 0
 15:05 
 0
 0
 0
 0
 10:48 
 0
 0
 0
 0
 12:17 
 0
 0
 0
 0
 7:43 
 0
 0
 0
 0
 5:56 
 0
 0
 0
 0
 16:40 
 0
 0
 0
 0
 13:12 
 0
 0
 0
 0
 11:34 
 0
 0
 0
 0
 11:49 
 0
 0
 0
 0
 10:26 
 0
 0
 0
 0
 1:01 
 0
 0
 0
 0
 12:16 
 0
 0
 0
 0
 7:48 
 0
 0
 0
 0
 11:57 
 0
 0
 0
 0
 11:47 
 0
 0
 0
 0
 11:39 
 0
 0
 0
 0
 10:22 
 0
 0
 0
 0
 11:31 
 0
 0
 0
 0
 11:40 
 0
 0
 0
 0
 11:36 
 0
 0
 0
 1
 4:49 
 0
 0
 0
 0
 12:24 
 0
 0
 0
 0
 18:56 
 0
 0
 0
 0
 5:04