SlowGuess/NAACL_2022_OCR huggingface dataset:
In our implementation, we use  $\mathrm{BERT}_{\mathrm{base}}$  (Devlin et al., 2019) and  $\mathrm{RoBERTa}_{\mathrm{large}}$  (Liu et al., 2019) as our backbone encoder for TSAR, with global and local encoders sharing parameters. Detailed hyperparameters are listed in Appendix B.
Following Zhang et al. (2020b), we report the Span F1 and Head F1 for RAMS dataset. Span F1 requires the predicted argument spans to fully
Table 1: Statistics of RAMS and WikiEvents datasets.
Method	Dev	Test
Span F1	Head F1	Span F1	Head F1
BERT-CRF	38.1	45.7	39.3	47.1
BERF-CRF\( _{TCD} \)	39.2	46.7	40.5	48.0
Two-Step	38.9	46.4	40.1	47.7
Two-Step\( _{TCD} \)	40.3	48.0	41.8	49.7
FEAE	-	-	47.40	-
\( TSAR_{base} \) (Ours)	45.23	51.70	48.06	55.04
BART-Gen	-	-	48.64	57.32
\( TSAR_{large} \) (Ours)	49.23	56.76	51.18	58.53
Table 2: Comparison between TSAR and other methods on RAMS dataset. Models above the double line are based on  $\mathrm{BERT}_{\mathrm{base}}$ . TSAR consistently outperforms others on Span F1 and Head F1. Compared with BART-Gen, TSAR improves 2.54 Span F1 in the test set.
match the golden ones, while Head F1 relaxes the constraint and evaluates solely on the head word of the argument span. The head word of a span is defined as the word that has the smallest arc distance to the root in the dependency tree. In addition, following Li et al. (2021), we report the Head F1 and Coref F1 scores for WikiEvents dataset. The model is given full credit in Coref F1 if the extracted argument is coreferential with the golden argument as used by Ji and Grishman (2008).
We compare TSAR with the following baselines. 1) BERT-CRF (Shi and Lin, 2019) is a tagging-based method, which adopts a BERT-based BIO-styled sequence labeling model. 2) Two-Step (Zhang et al., 2020b) is a span-based method, which first identifies the head word of possible argument span, and then extends to the full span. BERT-CRF $_{\text{TCD}}$  and Two-Step $_{\text{TCD}}$  refers to adopting Type-Constraint Decoding mechanism as used in (Ebner et al., 2020). 3) FEAE (Wei et al., 2021), Frame-aware Event Argument Extraction, is a concurrent work based on question answering. 4) BERT-QA (Du and Cardie, 2020c) is also a QA-based model. BERT-QA and BERT-QA-Doc extract run on sentence-level and document-level, respectively. 5) BART-Gen (Li et al., 2021) formulate the task as a sequence-to-sequence task and uses  $\text{BART}_{\text{large}}$  (Lewis et al., 2020) to generate corresponding arguments in a predefined format.
Table 2 illustrates the results in both dev and test set on RAMS dataset. As is shown, among models based on  $\mathrm{BERT}_{\mathrm{base}}$ , TSAR outperforms other previous methods. For example, TSAR yields an improvement of  $4.93 \sim 7.13$  Span F1 and
Method	Arg Identification	Arg Classification
Head F1	Coref F1	Head F1	Coref F1
BERT-CRF	69.83	72.24	54.48	56.72
BERT-QA	61.05	64.59	56.16	59.36
BERT-QA-Doc	39.15	51.25	34.77	45.96
TSARbase (Ours)	75.52	73.17	68.11	66.31
BART-Gen	71.75	72.29	64.57	65.11
TSARlarge (Ours)	76.62	75.52	69.70	68.79
$3.70 \sim 6.00$  Head F1 compared with the previous method in the dev set, and up to 8.76 Span F1 in the test set. Besides, among models based on large pre-trained language models, TSAR outperforms BART-Gen by 2.54 Span F1 and 1.21 Head F1 $^{2}$ . These results suggest that encoding the document in a two-stream way, and introducing AMR graphs to facilitate interactions, is beneficial to capturing intra-sentential and inter-sentential features, and thus improves the performance.
Moreover, we follow Li et al. (2021) to evaluate both argument identification and argument classification, and report the Head F1 and Coref F1. Identification requires the model to correctly detect the argument span boundary, while classification has to further correctly predict its argument role. As illustrated in Table 3, TSAR consistently outperforms others in both tasks. Compared with BART-Gen, TSAR improves up to 4.87/3.23 Head/Coref F1 for argument identification, and 5.13/3.68 Head/Coref F1 for argument classification. Similar results also appear among models based on  $\mathrm{BERT}_{\mathrm{base}}$  with  $5.69 \sim 36.37$  and  $11.95 \sim 33.34$  Head F1 improvement for identification and classification. These results show that TSAR is superior to other methods in not only detecting the boundary of argument spans, but also predicting their roles.
6.1 Cross-sentence Argument Extraction
Since there are multiple sentences in the document, some event arguments are located far away from the trigger, which highly increases the difficulty of
Table 3: Comparison between TSAR and other methods on WikiEvents dataset. Models above the double line are based on  $\mathrm{BERT}_{\mathrm{base}}$ . TSAR yields evident improvements in argument identification and classification sub-tasks. Compared with BART-Gen, TSAR improves Head F1 in argument classification by 5.13 score.
Method	d=-2	d=-1	d=0	d=1	d=2
BERT-CRF	14.0	14.0	41.2	15.7	4.2
Two-Step	15.6	15.3	43.4	17.8	8.5
FEAE	23.7	19.3	49.2	25.0	5.4
TSARbase (Ours)	24.3	21.9	49.6	24.6	11.9
BART-Gen	24.3	28.1	52.4	24.8	20.8
TSARlarge (Ours)	28.6	30.6	53.1	27.1	22.3
Table 4: Span F1 in RAMS dataset with different sentence distance between trigger and arguments. Most improvements by TSAR come from cross-sentence  $(d\neq 0)$  arguments extraction.
Method	Dev	Test
Span F1	Head F1	Span F1	Head F1
TSARlarge	49.23	56.76	51.18	58.53
- Global Encoder	46.71	54.26	48.21	55.49
- Local Encoder	48.43	55.44	48.69	56.82
- AMR-guided Graph	48.63	55.24	49.21	56.70
- Boundary Loss	47.93	55.14	50.47	57.75
Table 5: Ablation study on RAMS for  $\mathbf{TSAR}_{\mathrm{large}}$ . The score would decrease without any kind of module.
extraction. To explore the effect of handling such cross-sentence arguments of our TSAR, we divide the event arguments in RAMS dataset into five bins according to the sentence distance between arguments and trigger, i.e.,  $d = \{-2, -1, 0, 1, 2\}$ . We report the Span F1 on the RAMS dev set for different methods. As shown in Table 4, the Span F1 for cross-sentence arguments ( $d \neq 0$ ) is much lower than local arguments ( $d = 0$ ), suggesting the huge challenge to capture long-distance dependency between triggers and cross-sentence arguments. However, TSAR still surpasses other strong baselines. In detail,  $\mathrm{TSAR}_{\mathrm{base}}$  improves 0.4 and  $\mathrm{TSAR}_{\mathrm{large}}$  improves 0.7 F1 compared with the previous state-of-the-art, respectively. More importantly, when extracting cross-sentence arguments,  $\mathrm{TSAR}_{\mathrm{base}}$  and  $\mathrm{TSAR}_{\mathrm{large}}$  yield an improvement of up to 2.3 and 2.7 on average. The results support our claims that TSAR is good at capturing both intra-sentential and inter-sentential features, especially the long-distance between trigger and arguments.
We conduct an ablation study to explore the effectiveness of different modules in TSAR. Table 5 show the results on RAMS datasets for  $\mathrm{TSAR}_{\mathrm{large}}$ . We also provide results for  $\mathrm{TSAR}_{\mathrm{base}}$ , and those on WikiEvents datasets in Appendix C.
Firstly, we remove the global or local encoder in
2We use TSARlarge based on RoBERTalarge to compare with BART-Gen based on BARTlarge, as they are pre-trained on the same corpus with the same batch size and training steps.
Category	Examples	Errors
Two-step	TSAR
Wrong Span	It was Bush's administration participant, not [Obama]participant's, that negotiated meet the 2009 agreement from Iraq by Dec. 31, 2011.	86	81
Over-extract	The information minister alleged that oil smuggled smuggle into Turkey was bought by [the Turkish president's son]transporter, who owns an oil company ...	64	48
Partial	[280 victims]victim, including women, children and old people_victim ... The massacre_die is considered as one of the worst mass killings committed in Syria ...	47	57
Overlap	Richard, the man accused of punching_injury a [69-year-old_protester]victim_victim outside a Donald Trump rally in Asheville, NC on Monday, is vigorously ...	32	28
Wrong Role	The investigation found 100 people were linked to the transport_disperseseparate and ... It found the missile had been driven from [Russia]destination origin into an ...	46	19
Figure 4: Error analysis on RAMS dataset. The triggers are in bold with corresponding event types in green. The underlined spans refer to golden arguments, with their roles in blue. The [bracketed] spans denote the predicted arguments, with their roles noted in red. We illustrate the number of different kinds of errors for Two-step and our TSAR, which has 275 and 233 errors in total, respectively. Compared with Two-step, TSAR decreases errors in most error categories, especially for Wrong Role and Over-extract.
Figure 5: An extraction case, where an Attack event is triggered by stabbings with three arguments. TSAR manages to extract the cross-sentence argument Minnesota far from the trigger, while other methods fail.
the two-stream encoding module. As shown in Table 5, the removal causes drop in performance, e.g., 3.04 and 1.71 Head F1 drop on the test set without global and local encoder. It suggests the global and local encoders are complementary to each other, and both of them are necessary for TSAR.
Secondly, once we remove the AMR-guided interaction module, the Head F1 would decrease by 1.83 on the test set. It shows the semantic structure provided by AMR graphs is helpful to the arguments extraction of the document.
Finally, the removal of boundary loss causes the boundary information lost in span representations, which also leads to 1.62 and 0.78 Head F1 decrease on dev and test set.
6.3 Case Study
In this section, we show a specific case of the extraction results among different methods. As shown in Figure 5, stabbings triggers an Attack event with three arguments in color. Since Nine people is located near the trigger, all the methods correctly predict it as the target. However, extracting Minnesota and Dahir Adan asks for capturing long-distance dependency. Although Two-Step and BART-Gen wrongly predict the place as Iraq and Syria, and Two-Step even fails to extract the Attacker, TSAR manage to extract the cross-sentence arguments. It can be attributed to that our AMR-enhanced module catches Minnesota is the place of attack that is highly related to the trigger stabbings in semantics.
To further explore the errors made by different models and analyze the reasons in detail, we randomly choose 200 examples from the RAMS test set and compare the predictions with golden annotations manually. We divide the errors into five categories, which is shown in Figure 4. Wrong Span refers to assigning a specific role to a wrong span non-overlapped with the golden one. We find it is usually due to the negative words like not, and the coreference spans for the golden one. Over-extract denotes the model predicts an argument role while it does not exist in the document. Some extracted spans are the sub-strings of the golden spans (Partial), or have some overlaps with them (Overlap). These two kinds of errors are usually attributed to
the annotation inconsistency in the dataset, such as whether the adjective, quantifier, and article (e.g.,  $a$  and  $the$ ) before the noun should belong to the golden argument. Besides, the Partial error also usually occurs in cases where there is punctuation like a comma in the golden span as shown in Figure 4. Finally, though the model succeeds to identify the golden span, it can still assign wrong argument role to the span (Wrong Role). We compare the errors of Two-step $_{\text{TCD}}$  and TSAR $_{\text{base}}$ . We observe TSAR decrease the number of errors from 275 to 233, especially for Wrong Role and Over-extract, with 27 and 16 errors reduction, respectively.
It is challenging to extract event arguments from a whole document, owing to the long-distance dependency between trigger and arguments over sentences and the distracting context. To tackle these problems, we propose Two-Stream AMR-enhanced extraction model (TSAR). TSAR uses two-stream encoders to encode the document from different perspectives, followed by an AMR-guided interaction module to facilitate the document-level semantic interactions. An auxiliary boundary loss is introduced to enhance the boundary information for spans. Experiments on RAMS and WikiEvents datasets demonstrate that TSAR outperform previous state-of-the-art methods by a large margin, with 2.51 and 5.13 F1 improvements respectively, especially for cross-sentence argument extraction.
This paper is supported by the National Key R&D Program of China under Grand No.2018AAA0102003, the National Science Foundation of China under Grant No.61936012 and 61876004.
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A Abstract Meaning Representation (AMR) Graph
There are many AMR parsing approaches (Bevilacqua et al., 2021; Fernandez Astudillo et al., 2020; Wang et al., 2022; Chen et al., 2022). To obtain AMR semantic graphs with the align information between text spans and AMR nodes, we use the transition-based AMR parser proposed by Fernandez Astudillo et al. (2020), which is a state-of-the-art AMR parser and can achieve satisfactory results for downstream application (up to 81.3 Smatch on AMR2.0 data). As the number of AMR relation types is large, which results in too many demanded parameters, we follow Zhang and Ji (2021) to cluster the relation types into main categories as shown in Table 6.