Simple Applications of BERT for Ad Hoc Document Retrieval
Table 1: Results on test collections from the TREC Microblog Tracks, comparing BERT with selected neural ranking models. The first two blocks of the table contain results copied from Rao et al. (2019).
['[BOLD] Model', '[BOLD] 2011  [BOLD] AP', '[BOLD] 2011  [BOLD] P30', '[BOLD] 2012  [BOLD] AP', '[BOLD] 2012  [BOLD] P30', '[BOLD] 2013  [BOLD] AP', '[BOLD] 2013  [BOLD] P30', '[BOLD] 2014  [BOLD] AP', '[BOLD] 2014  [BOLD] P30']
The first two blocks of the table are copied from Rao et al. Results for all the neural models include interpolation with the original document scores. Rao et al. In contrast, MP-HCNN was explicitly designed with characteristics of tweets in mind: it significantly outperforms previous neural ranking models (see original paper for comparisons, not repeated here). We also copied results from Shi et al. Since we have directly copied results from previous papers, we did not conduct significance tests.