SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 9: Full structure F1 on FN 1.7.
Model	Frame Acc.
Peng et al. (2018)	88.6
KID (GloVe)	89.5
Jiang and Riloff (2021)	92.1
Su et al. (2021)	92.4
KID (BERT)	91.7
sequence generation task.
Jiang and Riloff (2021): a sentence-pair bert-based model using frame definitions.
Su et al. (2021): a BERT-based model for frame identification using both frame identification and frame semantic relations.
C.2 Empirical Results on FN 1.7
Table 9, 10, 11 list our results with comparing models. KID outperforms previous state-of-the-art except Su et al. (2021). FN 1.7 is the up-to-date extension version of FN 1.5 containing more fine-grained frames and FEs. However, there are only few models reporting their results on FN 1.7 and we hope that future work on frame semantic parsing can be more focused on FN 1.7.
C.3 Time Costs of FKG
The FKG is built over the full FrameNet containing more than 10,000 nodes while the intra-frame and inter-frame relations make the graph larger. Since we need to encode the full FKG when parsing a single sentence, it's necessary to explore the time costs of FKG. Results are shown in Table 12 and we can find the time encoding FKG is approximately  $20\%$  in the whole runtime and may slightly hurt the efficiency of our models. However, in inference time,
Table 10: Frame Acc. on FN 1.7.
Model	Precision	Recall	F1-score
open-SESAME (2017)	62	55	58
KID (GloVe)	69.2	73.3	71.2
Kalyanpur et al. (2020)	71	73	72
KID (BERT)	74.1	77.3	75.6
Table 11: Arg F1 on FN 1.7. Results of other models are obtained from Kalyanpur et al. (2020).
Model	Time cost (%)
KID (GloVe)	27.17
KID (BERT)	18.85
Table 12: Time cost of FKG. We list the proportion of time encoding FKG in whole runtime. Encoding FKG may slightly hurt the efficiency of models but is not the bottleneck of our models.
the representations of nodes in FKG are fixed and we can load the representations offline to reduce the inference time.
C.4 Statistical Analysis of KID on FN 1.5
For evaluating solidity of our model, we train KID with five random seeds. The average performances with deviation and results of significance testing are listed in Table 13. The significance testing is to show whether our model significantly outperforms previous state-of-the-art, and we do not conduct significance testing for KID (BERT) because we do not outperform Su et al. (2021) on frame accuracy. All  $p$ -values are less than 0.05 and even some  $p$ -values are less than 1e-3, which proves the solidity of our model.
Model	Metrics	seed	Avg. ± Dev.	p-value
s1	s2	s3	s4	s5
KID (GloVe)	Frame Acc.	89.14	88.90	89.12	89.48	88.55	89.04 ± 0.34	0.007
Arg F1	65.65	66.30	65.87	66.35	65.74	65.98 ± 0.32	*
Full structure F1	74.76	75.34	74.94	75.28	74.57	74.98 ± 0.33	*
KID (GloVe + exemplar)	Frame Acc.	89.74	89.56	89.75	89.90	89.93	89.78 ± 0.15	0.002
Arg F1	69.63	69.85	69.48	70.08	69.25	69.66 ± 0.32	0.033
Full structure F1	77.27	77.41	77.36	77.73	77.32	77.42 ± 0.18	*
KID (BERT)	Frame Acc.	91.81	91.92	91.81	91.74	91.63	91.78 ± 0.11	-
Arg F1	74.76	74.55	74.98	75.17	74.60	74.81 ± 0.26	0.028
Full structure F1	81.50	81.44	81.61	81.68	81.38	81.52 ± 0.13	*
Table 13: Statistical analysis of multiple runs on FN 1.5. We train our model with five different random seeds  $s_1 - s_5$  and the results with seed  $s_4$  are reported in Table 2. We both report the average performance with deviation and the results of significance testing, where * denotes the  $p$ -value is less than 1e-3.