SlowGuess/NAACL_2022_OCR huggingface dataset:
Categories	Relation Types
Spatial	location, destination, path
Temporal	year, time, duration, decade, weekday
Means	instrument, manner, topic, medium
Modifiers	mod, poss
Operators	op-X
Prepositions	prep-X
Sentence	snt
Core Roles	ARG0, ARG1, ARG2, ARG3, ARG4
Others	other relation types
B Hyperparameters Setting
We set the dropout rate to 0.1, batch size to 8, and train TSAR using Adam (Kingma and Ba, 2015) as optimizer with 3e-5 learning rate. We train TSAR for 50 epochs for RAMS dataset and 100 epochs for WikiEvents dataset. We search the boundary loss weight  $\lambda$  from  $\{0.05, 0.1, 0.2\}$ , and  $L$  from  $\{3, 4\}$ , and select the best model using dev set. Our code is based on Transformers (Wolf et al., 2020) and DGL libraries (Wang et al., 2019).
In the main body of the paper, we illustrate the results of the ablation study for  $\mathrm{TSAR}_{\mathrm{large}}$  on RAMS dataset. To thoroughly show the effect of different modules of TSAR, we also provide the results of the ablation study for  $\mathrm{TSAR}_{\mathrm{base}}$  on RAMS dataset. Table 7 shows the results on RAMS dataset, from which we can observe removing different modules would cause  $1.34 \sim 2.77$  Span F1 on test set.
Besides, we do ablation study on WikiEvents. As shown in Table 8, the Head F1 decreases by  $0.70 \sim 2.02$  and  $0.88 \sim 2.96$  for Arg Identification and Arg Classification sub-tasks respectively, once different modules are removed from  $\mathrm{TSAR}_{\mathrm{base}}$ . Similar conclusions can be drawn from the results of  $\mathrm{TSAR}_{\mathrm{large}}$ , which is shown in Table 9.
Table 6: Similar AMR relation types are clustered into the same relation category. The exception is that ARGx is still treated as an individual relation type.
Method	Dev	Test
Span F1	Head F1	Span F1	Head F1
TSARbase	45.23	51.70	48.06	55.04
- Global Encoder	43.05	50.90	45.29	53.62
- Local Encoder	44.63	51.34	46.50	53.26
- AMR-guided Graph	43.57	50.80	45.97	52.85
- Boundary Loss	44.42	51.08	46.72	53.91
Table 7: Ablation study on RAMS for  $\mathbf{TSAR}_{\mathrm{base}}$ . The score would decrease without any kind of module.
Method	Arg Identification	Arg Classification
Head F1	Coref F1	Head F1	Coref F1
TSARbase	75.52	73.17	68.11	66.31
- Global Encoder	73.50	72.23	65.15	64.07
- Local Encoder	74.40	72.62	67.11	65.41
- AMR-guided Graph	73.88	72.45	65.83	64.94
- Boundary Loss	74.82	72.50	67.23	65.95
Table 8: Ablation study on WikiEvents for  $\mathbf{TSAR}_{\mathrm{base}}$ . The performance of identification and classification would decrease without any kind of module.
Method	Arg Identification	Arg Classification
Head F1	Coref F1	Head F1	Coref F1
TSARlarge	76.62	75.52	69.70	68.79
- Global Encoder	74.12	72.80	67.54	66.41
- Local Encoder	74.60	73.32	68.08	66.88
- AMR-guided Graph	74.52	73.82	67.67	66.54
- Boundary Loss	75.50	74.05	68.60	67.33
Table 9: Ablation study on WikiEvents for  $\mathbf{TSAR}_{\mathrm{large}}$ . The performance of identification and classification would decrease without any kind of module.