SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 10: Perplexities measured on validation and test sets on WT2 and WT103.
Method	BLEU
Transformer Base (Vaswani et al., 2017)	27.8±0.2
+ FRAGE (Gong et al., 2018)	28.3±0.1
+ Ours	28.6±0.2
Table 11: BLEU scores for the WMT2014 Ee→De machine translation.
Method	F1
BERT (Devlin et al., 2018) + CRF	91.0±0.1
+ DEI (Li et al., 2020b)	91.8±0.1
+ Ours	92.5±0.0
Table 12: Named entity recognition results of baselines and the proposed model on CoNLL-03.
Deep FM	AutoInt
# P	B	+ T	+ Ours	B	+ T	+ Ours
10k	84.9±0.1	84.9±0.1	85.2±0.1	84.5±0.1	84.6±0.2	84.8±0.1
20k	85.0±0.1	84.9±0.1	85.2±0.1	84.7±0.1	84.8±0.2	84.8±0.1
30k	85.1±0.0	85.0±0.0	85.3±0.0	84.7±0.0	84.8±0.0	84.9±0.0
Table 13: Test AUC on the Movielens 1M.  $\# \mathrm{P}$  denotes the number of parameters,  $\mathbf{B}$  the baseline only trained with  $\ell_{\mathrm{main}}$  while  $^+\mathrm{T}$  denotes with  $\ell_{\mathrm{transfer}}$
Deep FM	AutoInt
# P	B	+ T	+ Ours	B	+ T	+ Ours
50k	79.7±0.2	79.8±0.1	80.0±0.1	79.3±0.1	79.5±0.2	79.7±0.1
20k	79.6±0.1	79.6±0.1	79.9±0.1	79.2±0.2	79.4±0.1	79.6±0.0
Table 14: Test AUC on the Criteo dataset with DeepFM and AutoInt.