text: To our knowledge, morphological information 
  in BERT heads has not been addressed, but with 
  the sparse attention variant by Correia et al. (2019) 
  in the base Transformer, some attention heads ap- 
  pear to merge BPE-tokenized words. For semantic 
  relations, there are reports of self-attention heads 
  encoding core frame-semantic relations (Kovaleva 
  et al., 2019), as well as lexicographic and common- 
  sense relations (Cui et al., 2020).  Several studies proposed classiﬁcation of attention 
  head types. Raganato and Tiedemann (2018) dis- 
  cuss attending to the token itself, previous/next 
  tokens and the sentence end. Clark et al. (2019) 
  distinguish between attending to previous/next to- 
  kens, [CLS], [SEP], punctuation, and "attending 
  broadly" over the sequence. Kovaleva et al. (2019) 
  propose 5 patterns shown in Figure 3. 
  
  The overall popularity of self-attention as an in- 
  terpretability mechanism is due to the idea that 
  "attention weight has a clear meaning: how much 
  a particular word will be weighted when comput- 
  ing the next representation for the current word" 
  (Clark et al., 2019). This view is currently debated 
  (Jain and Wallace, 2019; Serrano and Smith, 2019; 
  Wiegreffe and Pinter, 2019; Brunner et al., 2020), 
  and in a multi-layer model where attention is fol- 
  lowed by non-linear transformations, the patterns 
  in individual heads do not provide a full picture. 
  Also, while many current papers are accompanied 
  by attention visualizations, and there is a growing 
  number of visualization tools (Vig, 2019; Hoover 
  et al., 2019), the visualization is typically limited 
  to qualitative analysis (often with cherry-picked 
  examples) (Belinkov and Glass, 2019), and should 
  not be interpreted as deﬁnitive evidence. 