SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 7: The searching scope of hyperparameters.
k	[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
δ	[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
u	[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
Table 8: The searching scope of BSNMS hyperparameters.
We construct our FewNERD-nested via ACE05 (Walker et al., 2006), GENIA (Ohta et al., 2002) and the origin FewNERD datasets. GENIA is a biological named entity recognition dataset, which contains 5 kinds of entities, 'DNA', 'Protein', 'cell_type', 'RNA' and 'cell_line', and all of these entity types are not included in the FewNERD. We
Figure 5: The performance drops in noisy situations when increasing  $r_{noise}$ .  $\Delta \mathrm{F1\%}$  is the F1 reduction percent over  $r_{noise} = 0$ .
partition the sentences in GENIA into two groups,  $\mathcal{G}_1$  and  $\mathcal{G}_2$ .  $\mathcal{G}_1$  consists of sentences with nested entities (4,744 sentences in total), and  $\mathcal{G}_2$  consists of sentences without nested entities (11,924 sentences in total). We utilize sentences in  $\mathcal{G}_1$  and  $\mathcal{G}_2$  to construct the query and support set of the GENIA task, respectively. Our FewNERD-nested contains 2000 5-way  $5\sim 10$ -shot test tasks in total, where  $r_{nested}$  percent tasks are from GENIA, and the remained tasks are from FewNERD-INTRA. We use ACE05 to construct the validation set. ACE05 is a widely used named entity recognition dataset, which contains 7 coarse-grained entity types, 'FAC', 'PER', 'LOC', 'VEH', 'GPE', 'GPE', 'WEA' and 'ORG'. The 'LOC', 'PER', 'ORG' and 'FAC' are not included in the training set of FewNERD-INTRA, and therefore we use them (28 fine-grained entity types in total) and sample 1000 nested tasks as the validation dataset to tune the  $k$ ,  $\delta$  and  $u$  of BSNMS. The search scope is included in the Table 8. In this nested situation, we finally set the  $k$ ,  $\delta$  and  $u$  to 0.1, 0.1 and 0.4 respectively.
C Robustness in the Noisy Situation
FSSL methods tend to be seriously influenced by the noise in the support set, since they make the prediction based on only limited annotated examples. To explore the robustness of ESD in the noisy situation, we construct FewNERD-noise. In FewNERD-noise, we disturb each 5 way  $5 \sim 10$  shot FewNERD-INTRA task with a noisy ratio  $r_{noise}$ , which means there are nearly  $r_{noise}$  percent entities in the support set are mislabeled. As illustrated in the right part of Figure 5, with  $r_{noise}$  increasing, the performance of ESD drops less than baselines, which further shows the superiority of
our methods.
D A Detail Case Study of BSNMS
For span conflicts resolution, we propose a postprocessing method BSNMS. A step by step demonstration of BSNMS is shown in Figure 6.
Figure 6: A step by step processing process of BSNMS with beam size=2. For clarity, we set the filter threshold  $\delta$  to 0.6, and suppose the span score is always decayed by the overlapped spans with a constant decayed score  $-0.1$ . STEP1: create 2 (beam size) states with spans having larger predicted scores; STEP2: extend all states (i.e., states 1 and 3) in the beam to 1-3, 1-2, 1-4, 3-1, 3-4 and 3-2. Compute DS of the new added span and PS of the new state. As beam size=2, prune states 1-4, 3-4 and 3-2 according to their lower PS. The state 3-1 is dropped since it is equivalent with the state 1-3; STEP3: extend states in the beam (1-3 and 1-2) to 1-3-4, 1-3-2, 1-2-3, 1-2-4, and compute their DS and PS. Filter states 1-3-2, 1-2-3, 1-2-4 since their DS are not greater than  $\delta$ , i.e., low DS; STEP4: extend states in the beam (only 1-3-4 since the state 1-2 has been extended before) to 1-3-4-2, and filter 1-3-4-2 due to its low DS; STEP5: all states in the beam can not extend any more, and select the final state (1-2 in this case) with the largest PS.