SlowGuess/NAACL_2022_OCR huggingface dataset:
by far,  $s$  denotes embedding of sentences and event argument candidates, and  $y_{e}$  denotes label of argument candidate  $e$  in current step.  $y_{e} = 1$  means  $e$  is the ground truth argument corresponding to current step event role, otherwise,  $y_{e} = 0$ .
4.6 Model Training
To train the above four components, we leverage the multi-task learning method (Collobert and Weston, 2008) and integrate the four corresponding loss functions together as the following:
$$
\mathcal {L} = \lambda_ {1} \mathcal {L} _ {n e} + \lambda_ {2} \mathcal {L} _ {d r e} + \lambda_ {3} \mathcal {L} _ {p r e d} + \lambda_ {4} \mathcal {L} _ {a} \tag {11}
$$
where the  $\lambda_{i}$  pre-set to balance the weight among the four components.
In this section, we report the experimental results to prove the effectiveness of our proposed method. In summary, the experiments could answer the following three questions:
- To what degree does the ReDEE model outperform the baseline DEE methods?
- How well does ReDEE overcome across-sentence and multi-event issues?
- In what level does the each key component of ReDEE contribute to the final performance?
DEE is a relatively new task and there are only a few datasets published. In our experiments we adopt two public Chinese datasets, i.e. ChiFinAnn (Zheng et al., 2019) and DuEE-fin (Li, 2021).
ChiFinAnn includes 32,040 documents with 5 types of events, involving in equity-related activities for the financial domain. Statistics show that about  $30\%$  of the documents contain multiple event records. We randomly split the dataset into train/dev/test sets in the ratio of 8/1/1. Readers can refer to the original paper for details.
DuEE-fin is also from the financial domain with around 11,900 documents in total. The dataset is downloaded from an online competition website*. Since there is no ground truth publicly available for the test set, we can only submit our extracted results to the website as a black-box online evaluation. Compared to ChiFinAnn, there are two
differences. The DuEE-fin dataset has 13 different event types and its test set includes a large size of document samples that do not have any event records, which both make it more complicated. We get the distribution information of the dataset from Appendix A.1.
Five different baseline models are taken into consideration: 1) DCFEE (Yang et al., 2018), the first model proposed to solve DEE task. 2) Doc2EDAG (Zheng et al., 2019), proposed an end-to-end model which transforms DEE as directly filling event tables with entity-based path expending. 3) DE-PPN (Yang et al., 2021), a pipeline model firstly introducing the non-autoregressive mechanism. 4) GIT (Xu et al., 2021b), a model using heterogeneous graph interaction network as encoder and maintaining a global tracker during the decoding process. 5) PTPCG (Zhu et al., 2021), a light-weighted and latest DEE model.
For evaluation metrics, we use precision, recall, and F1 score at the entity argument level for fair comparison with baselines. The overall "Avg" in the result tables denotes the micro average value of precision, recall, and F1 score. We conduct several offline evaluations for ChiFinAnn, but only an online test for DuEE-fin.
In our implementation, for text processing, we consistently set the maximum sentence number and the maximum sentence length as 128 and 64 separately. We use BERT encoder in the EER component for fine-tuning and Roberta-chinese-wwm (Yiming et al., 2020) as the pre-trained model. Both RAAT-1 and RAAT-2 have four layers of identical blocks. More training details can be found in Appendix A.5.
Overall Performance Table 3 shows the comparison between baselines and our ReDEE model on the ChiFinAnn dataset. The ReDEE can achieve the state-of-the-art performance in terms of micro average recall and F1 scores on almost every type of events (i.e. EF, ER, EU, EO, EP), consistent with the Avg. results increased by  $1.5\%$  and  $1.6\%$  respectively. Our model also performs competitively well on precision results.
Table 4 shows the comparison results of our model with baselines on the developing set of
*https://aistudio.baidu.com/aistudio/competition/detail/46
Model	EF	ER	EU	EO	EP	Avg
P.	R.	F1.	P.	R.	F1.	P.	R.	F1.	P.	R.	F1.	P.	R.	F1.	P.	R.	F1.
DCFEE-S†	61.1	37.8	46.7	84.5	86.0	80.0	60.8	39.0	47.5	46.9	46.5	46.7	64.2	49.8	56.1	67.7	54.4	60.3
DCFEE-M†	44.6	40.9	42.7	75.2	71.5	73.3	51.4	41.4	45.8	42.8	46.7	44.6	55.3	52.4	53.8	58.1	55.2	56.6
Greedy-Dec†	78.5	45.6	57.7	83.9	75.3	79.4	69.0	40.7	51.2	64.8	40.6	50.0	82.1	40.4	54.2	80.4	49.1	61.0
Doc2EDAG†	78.7	64.7	71.0	90.0	86.8	88.4	80.4	61.6	69.8	77.2	70.1	73.5	76.7	73.0	74.8	80.3	75.0	77.5
GIT†	78.9	68.5	73.4	92.3	89.2	90.8	83.9	66.6	74.3	80.7	72.3	76.3	78.6	76.9	77.7	82.3	78.4	80.3
DE-PPN♠	78.2	69.4	73.5	89.3	85.6	87.4	69.7	79.9	74.4	81.0	71.3	75.8	83.8	73.7	78.4	-	-	-
PTPCG♠	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	88.2	69.1	79.4
ReDEE(ours)	78.0	70.6	74.1	91.1	90.3	90.7	82.5	69.2	75.3	83.7	73.1	78.1	81.7	78.6	80.1	84.0	79.9	81.9
Table 3: Comparison of event extraction between baselines and our ReDEE model on the ChiFinAnn dataset. The missing parts are caused by the inaccessibility of baseline codes. †: results from (Xu et al., 2021b); ♠: results from (Yang et al., 2021); ⋆: results from (Zhu et al., 2021).
Model	Dev	Online test
P.	R.	F1.	P.	R.	F1.
Doc2EDAG♣	73.7	59.8	66.0	67.1	51.3	58.1
GIT♣	75.4	61.4	67.7	70.3	46.0	55.6
PTPCG♣	71.0	61.7	66.0	66.7	54.6	60.0
ReDEE(ours)	77.0	72.0	74.4	69.2	57.4	62.8
Table 4: Comparison of event extraction between baselines and our ReDEE model on the DuEE-fin dataset.  $\clubsuit$  results from (Zhu et al., 2021).
Model	I	II	III	IV
DCFEE-S†	64.6	70.0	57.7	52.3
DCFEE-M†	54.8	54.1	51.5	47.1
Greedy-DEC†	67.4	68.0	60.8	50.2
Doc2EDAG†	79.6	82.4	78.4	72.0
GIT†	81.9	85.7	80.0	75.7
ReDEE(ours)	83.9	85.8	81.7	77.9
Table 5: F1 scores on four sets growing with average number of sentences involved in event records. †: results from (Xu et al., 2021b).
DuEE-fin and its online testing. Seeing from former results, our model outperforms in a great leap by increasing  $6.7\%$  on F1 score. For the online testing evaluation, our model has a distinct growth of  $2.8\%$  on F1 score than the baselines. This experiment demonstrates our model could achieve a superior performance than existing methods.
Argument Scattering The across-sentence issue widely exists in datasets. By our statistics, the training sets of ChiFinAnn and DuEE-fin have about  $98.0\%$  and  $98.9\%$  records that scatter across sentences respectively. To evaluate the performance of our model in different argument scattering degree, we compute the average number of sentences involved in records for each document and sort them in the increasing average number order. Then, all documents for testing are evenly divided into four sets, namely, I, II, III and IV, which means the I set is a cluster of documents that have the smallest
average number of involved sentences while the IV set has the largest ones. According to table 5, our model outperforms other baseline models in all settings, and meets the largest growth of  $2.2\%$  F1 score in IV, the most challenging set of all. It indicates that our model is capable of capturing longer dependency of records across sentences via relation dependency modeling, thus alleviating the argument scattering challenge.
Single vs. Multi Events To illustrate how well our model performs in the multi-event aspect, we split the test set of ChiFinAnn into two parts: one for documents with single event record, and the other for documents including multiple events. Table 6 shows the comparison results of all baselines and ReDEE. We find ReDEE performs much better in the multi-event scenario and outperforms baseline models dramatically in all five event types, improving ranging from  $1.9\%$  to  $3.2\%$  F1 scores. The results suggest that our relation modeling method is more effective to overcome the multi-event issue than existing baseline models.
To probe the impact of RAAT structure for different components in ReDEE, we conduct ablation studies on whether to use RAAT or vanilla transformer.
In this experiment, we implement tests on three variants: 1) -RAAT-1 substitutes the RAAT in the ESE component with vanilla transformer. 2) -RAAT-2 substitutes the RAAT in the event record generation module with vanilla transformer. 3) -RAAT-1&2 substitutes the RAATs in both the above places with vanilla transformers, so that our model degrades to only import a relation extraction task via multi-task learning.
The results in Table 7 indicate that both two RAATs have positive influence on our model. Especially on ChiFinAnn, RAAT-2 makes more con
Model	EF	ER	EU	EO	EP	Avg
S.	M.	S.	M.	S.	M.	S.	M.	S.	M.	S.	M. & M.
DCFEE-S†	55.7	38.1	83.0	55.5	52.3	41.4	49.2	43.6	62.4	52.2	69.0	50.3
DCFEE-M†	45.3	40.5	76.1	50.6	48.3	43.1	45.7	43.3	58.1	51.2	63.2	49.4
Greedy-Dec†	74.0	40.7	82.2	50.0	61.5	35.6	63.4	29.4	78.6	36.5	77.8	37.0
Doc2EDAG†	79.7	63.3	90.4	70.7	74.7	63.3	76.1	70.2	84.3	69.3	81.0	67.4
GIT†	81.9	65.9	93.0	71.7	82.0	64.1	80.9	70.6	85.0	73.5	87.6	72.3
DE-PPN♠	82.1	63.5	89.1	70.5	79.7	66.7	80.6	69.6	88.0	73.2	-	-
PTPCG♣	-	-	-	-	-	-	-	-	-	-	88.2	69.1
ReDEE(ours)	79.7	69.1	92.7	73.6	79.9	69.2	81.6	73.7	86.3	76.5	87.9	75.3
Table 6: Comparison of event extraction between singular (S.) and multiple (M.) event documents on the ChiFinAnn. †: results from (Xu et al., 2021b);  $\spadesuit$  : results from (Yang et al., 2021);  $\clubsuit$  : results from (Zhu et al., 2021).
Model	ChiFinAnn	DuEE-fin
P.	R.	F1.	P.	R.	F1.
ReDEE	84.0	79.9	81.9	69.2	57.4	62.8
-RAAT-1	+0.4	-1.1	-0.4	+1.5	-1.7	-0.5
-RAAT-2	+1.3	-2.4	-0.7	+0.8	-3.2	-1.7
-RAAT-1&2	-3.1	-0.1	-1.5	-1.3	-5.1	-3.7
Table 7: Ablation studies on ReDEE variants for RAAT.
ribution than RAAT-1, with a decrease of  $0.7\%$  versus  $0.4\%$  in F1 scores once been substituted. After replacing both two RAATs, the value of relation extraction task becomes more weak and the model encounters a  $1.5\%$  drop in F1 score. When it comes to DuEE-fin, a similar phenomenon can be observed that both the RAATs can contribute positively to our model.
In this paper, we investigate a challenging task of event extraction at document level, towards the across-sentence and multi-event issues. We propose to model the relation information between event arguments and design a novel framework ReDEE. This framework features a new RAAT structure which can incorporate the relation knowledge. The extensive experimental results can demonstrate the effectiveness of our proposed method which makes the state-of-the-art performance on two benchmark datasets. In the future, we will make more efforts to accelerate training and inference process.
We thank the anonymous reviewers for their careful reading of our paper and their many insightful comments and suggestions. This work was supported by Tencent Cloud and Tencent Youtu Lab.
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Event Type	#Train.	#Dev.
ShareRedemption	1309	243
FinanceDeficit	1062	163
Pledge	1027	160
EnterpriseAcquisition	934	142
BidWin	915	134
ExecutiveChange	901	134
ShareholderHoldingDecrease	876	147
PledgeRelease	728	118
CorporateFinace	535	72
CompanyListing	482	82
ShareholderHoldingIncrease	321	62
CompanyBankruptcy	236	44
Admonition	172	32
Total	9498	1533
In the appendix, we incorporate the following details that are omitted in the main body due to the space limit.
A.1 Distribution of Event Type DuEE-fin
Table 8 shows the complete event type and corresponding distribution of DuEE-fin dataset. Overall, there are 13 event types in total with uneven distribution. Only train and development sets are shown since test set is not publicly available.
A.2 Complete Relation Triples
Table 9 demonstrates the complete of relation triples of the document event extraction example shown in Figure 1.
Entities in blue are involved in both two event records, while those in green and orange are exclusive to record 1 and 2 respectively. Heavy coupling of arguments among events increases the difficulty of multi-event issue.
A.3 Relation Statistics for ChiFinAnn
Table 10 shows the relation statistics of ChiFinAnn dataset. There are 85 relation types in total, and train, development, and test sets have similar pattern in distribution.
A.4 Case Study
Figure 4 shows the prediction results of our model and the best baseline model GIT on the example in Figure 1. Compared with the ground truth, our model correctly predicts all event arguments except one, while GIT only captures one event, with an argument missed. This example explicitly shows the superiority of our model in dealing with multi-events issue.
Table 8: Distribution of Duee-fin dataset.
Event-related Relations between Entities Table (Two Examples)
Head Entity	Tail Entity	Relation
[ORG1]	[ORG2]	Pledger2Pledgerge
[ORG1]	[SHARE1]	Pledger2PledgedShares
[ORG1]	[SHARE2]	Pledger2TotalHoldingShares
[ORG1]	[RATIO1]	Pledger2TotalHoldingRatio
[ORG1]	[TIME1]	Pledger2StartDate
[ORG1]	[TIME2]	Pledger2ReleaseDate
[SHARE1]	[ORG2]	PledgedShares2Pledgerge
[SHARE1]	[SHARE2]	PledgedShares2TotalHoldingShares
[SHARE1]	[RATIO1]	PledgedShares2TotalHoldingRatio
[SHARE1]	[TIME1]	PledgedShares2ShortDate
[SHARE1]	[TIME2]	PledgedShares2ReleaseDate
[ORG2]	[SHARE2]	Pledger2TotalHoldingShares
[ORG2]	[RATIO1]	Pledger2TotalHoldingRatio
[ORG2]	[TIME1]	Pledger2StartDate
[ORG2]	[TIME2]	Pledger2EndDate
[SHARE2]	[RATIO1]	TotalHoldingShares2TotalHoldingRatio
[SHARE2]	[SHARE2]	TotalHoldingShares2TotalPledgedShares
[SHARE2]	[TIME1]	TotalHoldingShares2StartDate
[SHARE2]	[TIME2]	TotalHoldingShares2ReleaseDate
[RATIO1]	[SHARE2]	TotalHoldingRatio2TotalPledgedShares
[RATIO1]	[TIME1]	TotalHoldingRatio2StartDate
[RATIO1]	[TIME2]	TotalHoldingRatio2ReleaseDate
[TIME1]	[TIME2]	StartDate2ReleaseDate
[ORG1]	[ORG3]	Pledger2Pledgerge
[SHARE2]	[ORG3]	PledgedShares2Pledgerge
[ORG3]	[SHARE2]	Pledger2TotalPledgedShares
[ORG3]	[RATIO1]	Pledger2TotalHoldingRatio
[ORG3]	[TIME2]	Pledger2StartDate
Table 9: Complete relation triplets.
A.5 More Training Settings
For all native transformers and RAATs, the dimensions of hidden layers and feed-forward layers are set to 768 and 1,024 respectively. During training, we set the learning rate  $lr = 5e^{-5}$ , batch size  $b = 64$ . The four loss weights are set to  $\lambda_1 = \lambda_3 = 0.05$ ,  $\lambda_2 = 1.0$ ,  $\lambda_4 = 0.95$ . We use 8 V100 GPUs and set gradient accumulation steps to 8. The train epoch are set to 100, and the best epoch are selected by the best validation score on development set for the evaluation of test set. And we use Adam to optimize the whole learning task.
Relation Statistics for ChiFinAnn
Relation type	#Train.	#Dev.	#Test.
Pledger2PledgedShares	20002	2567	2299
Pledger2Pledgee	20002	2576	2299
PledgedShares2Pledgee	20002	2576	2299
StartDate2EndDate	19615	2239	1877
Pledger2TotalHoldingShares	18552	2412	2173
PledgedShares2TotalHoldingShares	18552	2412	2173
Pledgee2TotalHoldingShares	18552	2412	2173
TotalHoldingShares2TotalHoldingRatio	17403	2416	2162
Pledger2TotalHoldingRatio	16465	2180	1923
PledgedShares2TotalHoldingRatio	16465	2180	1923
Pledgee2TotalHoldingRatio	16465	2180	1923
Pledger2StartDate	15839	2247	2047
PledgedShares2StartDate	15839	2247	2047
Pledgee2StartDate	15839	2247	2047
TotalHoldingShares2StartDate	15237	2296	2106
EquityHolder2StartDate	14725	1423	1058
Pledger2TotalPledgedShares	14549	2024	1842
PledgedShares2TotalPledgedShares	14549	2024	1842
Pledgee2TotalPledgedShares	14549	2024	1842
TotalHoldingShares2TotalPledgedShares	14369	1999	1813
EquityHolder2EndDate	14357	1280	881
EquityHolder2TradedShares	14245	1269	886
TradedShares2EndDate	14003	1226	843
TradedShares2StartDate	13749	1202	836
TotalHoldingRatio2TotalPledgedShares	13221	1823	1653
TotalHoldingRatio2EndDate	13093	2023	1837
Pledger2ReleasedDate	11215	873	707
PledgedShares2ReleasedDate	11215	873	707
Pledgee2ReleasedDate	11215	873	707
TotalHoldingRatio2ReleasedDate	10949	855	698
TotalPledgedShares2StartDate	10451	1712	1596
TotalHoldingRatio2ReleasedDate	9913	775	630
TotalPledgedShares2ReleasedDate	9472	715	609
StartDate2releasedDate	7106	586	484
EquityHolder2LaterHoldingShares	6317	507	346
TradedShares2LaterHoldingShares	6317	507	346
Pledger2EndDate	6189	1062	1806
PledgedShares2EndDate	6189	1062	1806
Pledgee2EndDate	6189	1062	1806
TotalHoldingShares2EndDate	6125	1046	1048
EndDateLaterHoldingShares	6094	469	304
StartDate2LaterHoldingShares	5885	446	309
Relation type	#Train.	#Dev.	#Test.
TotalHoldingRatio2EndDate	5185	898	866
EquityHolder2AveragePrice	3732	323	164
TradedShares2AveragePrice	3732	323	164
TotoalPledgedShares2EndDate	3691	772	823
EndDate2AveragePrice	3623	310	156
StartDate2AveragePrice	3551	306	149
EndDate2ReleaseDate	2717	265	215
CompanyName2LowestTradingPrice	2455	815	1219
CompanyName2RepurchasedShares	2447	812	1219
CompanyName2HighestTradingPrice	2446	811	1216
HighestTradingPrice2LowestTradingPrice	2431	801	1205
LowestTradingPrice2RepurchasedShares	2422	800	1207
HighestTradingPrice2RepurchasedShares	2413	796	1201
LaterHoldingShares2AveragePrice	1703	106	61
CompanyName2RepurchaseAmount	1512	617	1803
LowestTradingPrice2RepurchaseAmount	1492	605	1068
CompanyName2ClosingDate	1488	586	998
HighestTradingPrice2RepurchaseAmount	1482	603	1066
RepurchasedShares2ClosingDate	1482	585	989
RepurchasedShares2RepurchaseAmount	1479	602	1068
LowestTradingPrice2ClosingDate	1463	574	984
HighestTradingPrice2ClosingDate	1454	570	980
EquityHolder2FrozeShares	1361	324	330
EquityHolder2LegalInstitution	1361	324	330
FrozeShares2LegalInstitution	1361	324	330
EquityHolder2TotalHoldingShares	1197	307	293
FrozeShares2TotalHoldingShares	1197	307	293
LegalInstitution2TotalHoldingShares	1197	307	293
EquityHolder2TotalHoldingRatio	1069	263	269
FrozeShares2TotalHoldingRatio	1069	263	269
LegalInstitution2TotalHoldingRatio	1069	263	269
FrozeShares2StartDate	976	221	222
LegalInstitution2StartDate	976	221	222
ClosingDateRepurchaseAmount	811	436	882
FrozeShares2EndDate	354	54	38
LegalInstitution2EndDate	354	54	38
EquityHolder2UnfrozeDate	235	8	18
FrozeShares2UnfrozeDate	235	8	18
LegalInstitution2UnfrozeDate	235	8	18
TotalHoldingShares2UnfrozeDate	194	8	18
TotalHoldingRatio2UnfrozeDate	163	7	17
StartDate2UnfrozeDate	87	4	7
EndDate2UnfrozeDate	30	2	1
Total	621010	83502	80829
Table 10: Relation statistics of ChiFinAnn dataset.