Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction
Authors: Hao Li
Yanan Cao
Yubing Ren
Fang Fang
Lanxue Zhang
Yingjie Li
Shi Wang
Venue: EMNLP 2023 Findings (2023)
Keywords: event argument extraction
intra-event dependency
inter-event dependency
dependency-aware graph network

Abstract: Event argument extraction is critical to various natural language processing tasks for providing structured information. Existing works usually extract the event arguments one by one, and mostly neglect to build dependency information among event argument roles, especially from the perspective of event structure. Such an approach hinders the model from learning the interactions between different roles. In this paper, we raise our research question: How to adequately model dependencies between different roles for better performance? To this end, we propose an intra-event and inter-event dependency-aware graph network, which uses the event structure as the fundamental unit to construct dependencies between roles. Specifically, we first utilize the dense intra-event graph to construct role dependencies within events, and then construct dependencies between events by retrieving similar events of the current event through the retrieval module. To further optimize dependency information and event representation, we propose a dependency interaction module and two auxiliary tasks to improve the extraction ability of the model in different scenarios. Experimental results on the ACE05, RAMS, and WikiEvents datasets show the great advantages of our proposed approach.

Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction
Hao Li Yanan Cao Yubing Ren
Fang Fangl., Lanxue Zhang Yingjie Li Shi Wang' Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
2 School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China 3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China lihao1 1chaa1998@i11 aww.iieeac..oo ac
Abstract
type: mexemeestrraapprrppoooooiiirsrss
Origin Russi
Passenger
details shipment armm
Event argument extraction critical various natural language processing tasks for providing structured information. Existing works usually extract the event arguments one by one, and mostly neglect to build dependency informa- tion among event argument roles, especially from the perspective of event structure. Such an approach hinders the model from learning the interactions between different roles. In this paper, raise research question: How to adequately model dependencies between dif- ferent roles for better performance? To this end, we propose intra-event and inter-event dependency-awant graph network, which uses the event structure as the fundamental unit to construct dependencies between roles. Specif- ically, we first utilize the dense intra-event graph construct role dependencies within events, and then construct dependencies be- tween events by retrieving similar events current event through the retrieval module. To further optimize dependency information and event representation, we propose a dependency interaction module and two auxiliary tasks to improve the extraction ability of the model in different scenarios. Experimental results the ACE05, RAMS, and WikiEvents datasets show the great advantages of our proposed approach.
Transporter
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Transporter Qutaris paid smuggle shipments Chinese made
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Figure I: Two examples of event argument extraction from RAMS dataset. Trigger words are included special tokens <D> and </t>. Underlined words denote arguments and ares denote roles.
Typical efforts in EAE can be roughly divided into classification-basedd models (Zhang et al., 2020; Huang and Jia, 2021; Xu et al., 2022: Ma et al., 2022; Ren al., 2022), Question Answer- ing (QA)-based models (Du and Cardic, 2020; Liu et al., 2020; Li et al., 2020; Wei et al., 2021; Liu al., 2021), and generation-based models (Li et al.. 2021; Lu et al., 2021; Hsu et al., 2022; Ren et al., 2023). In these works, arguments are usually ex- tracted one by one, which mostly neglects to build dependency information among argument roles, es- pecially from the perspective of event structure. For example, classification-basse methods typically identify candidate spans and classify their roles independently; QA-based models recognize the boundaries of arguments with rolc-specific ques- tions; Generation methods need to construct role-specific templates for argument filling. Al- though the state-of-the-art (SOTA) method (Ma et al., 2022) extracts all arguments of event end- to-end, it still does not explicitly capture depen- dency among roles.
1 Introduction
Event Argument Extraction (EAE) aims to identify the arguments of given event and recognize the specific roles they play, which is one of the impor- tant subtasks of Event Extraction (EE). As illus- trated by the example in Figure 1, given dispers- eseparate event triggered by shipment, an event argument extractor expected identify Russia, arms, Armenia as the event arguments and predict their roles as (Transporter, Origin), Passenger, and Destination, where the Russia argument plays the roles of Transporter and Origin respectively.
However, the roles of different arguments within the same event or between similar events clearly
"Corresponding Author.

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depend on each other. Firstly, a complete event usually consists of multiple closely related roles, and establishing dependencies between them can provide intra-event core cues for each other. Fur- thermore, we count that 11.36% of arguments in RAMS (Ebner et al., 2020) have multiple roles, in which case modeling dependencies is more vi- tal. As shown in Figure 1, the Russia plays the roles of Transporter and Origin, respectively. It is intuitive that capturing the dependencies between Transporter and Origin and and jointly learning their se- mantic information can result in mutual gain. Sec- ondly, similar events may have similar event struc- tures and share partial roles, so modeling depen- dencies between roles in similar events can provide inter-event role correlation clues. According to our statistics, 76.98% and 44.00% event types in the ontologies of RAMS and WikiEvents (Li et al.. 2021) datasets share more than three roles, respec- tively. The disperseseparute event and smuggleex- tract event in Figure both describe the event of arms shipments between countries, and they share the roles of Transporter, Origin, and Destination, where utilizing the correlation between these two events can be mutually beneficial. Therefore, cap- turing role dependencies within the same event is essential, and modeling role dependencies between similar events also crucial.
dependency-aware graph network for EAE, which can help learn rich role semantic infor- mation without using manual label templates.
When constructing the inter-event graph, we further introduce retrieval module to retrieve the most similar event to the current event from the memory unit.
We conduct experiments on three widely used EAE datasets. Through experiments and vi- sual analysis, our model achieves new SOTA due to the dependency modeling, demonstrat- ing its effectiveness.
Related Works
Sentence-Level Event Extraction SEE extracts the event trigger and its arguments from a single sentence. It has achieved great progress in previ- ous studies. Li et al. (2013) employ various hand- designed features to extract events. With the pop- ularity of deep learning, researchers use various neural networks to extract events, such as convolu- tional neural network (CNN)-based models (Chen et al., 2015; Nguyen and Grishman, 2015), recur- rent neural network (RNN)-based models (Nguyen et al., 2016; Sha et al., 2018) and graph neural networks (GNN)-based models (Liu et al., 2018). As pre-trained language models (PLM) have been proven to be powerful in language understand- ing and generation (Devlin al., 2019), Yang et al. (2019); Wadden et al. (2019); Du and Cardic (2020); Wang et al. (2021); Lu al. (2021); Hsu et al. (2022); Liu et al. (2022); Dai et al. (2022) use PL--basee methods to extract events.
Built on these motivations, we raise our research question: How to adequately model dependencies between different roles for better performance? To this end, we propose intra-event and inter- Event Dependency-awerr aware Graph network for EAE (EDGE), which uses the event structure the fun- damental unit to build dependencies. Specifically, we first utilize the dense intra-event graph to cap- ture the role dependencies within events. Further- more, we propose a retrieval module to store and retrieve similar events of the current event, using the retrieved intra-event graph to assist in construct- ing inter-event graph for capturing dependencies between events. To propagate the intra-event and inter-event dependency and filter redundant infor- mation, we propose dependency interaction mod- ule to fully model the dependency information at different granularities. Finally, to further optimize the event representation, we propose two auxiliary tasks to improve the argument extraction ability of the model in different scenarios.
Document-Level Event Extraction Considering that real-world events are often distributed across sentences, DEE has attracted more attention re- cently. Different from SEE, DEE extracts the event trigger and its arguments from the whole document. On the task level, most of these works fall into three categories: (1) classification-based models (2) QA models (3) generation- based models. Specifically, Zhang et al. (2020); Xu et al. (2021); Huang and Jia (2021); Huang and Peng (2021); Xu et al. (2022); Ma et al. (2022); Ren et al. (2022) employ traditional classification paradigms to iden- tify arguments from text and classify the roles they play in events; Wei al. (2021); Liu et al. (2021) treat DEE as extractive question answering task, where the extracted arguments are based on care- fully constructed natural questions; With the help
We summarize our contributions as follows:
We propose an intra-event and inter-event

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Event Strucbure Constraint
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Inter Event Retriever
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Argument Extratiion
Figure 2: The overall architecture of EDGE. We specific example show the entire process and mark the text description under the specific module. We delineate the real similar and dissimilar events in the memory unit. Furthermore, colors distinguish different argument roles, solid lines with arrows indicate the flow data, and dashed lines indicate dependency interactions. Finally, the specific extraction results of this example are described.
the pre-trained Bncoder- Transformer ar- chitecture (Lewis et al., 2020), Li et al. (2021); Du et al. (2022); Ren et al. (2023) convert the extrac- tion task to a sequenceeto-sequeee generation task, and get the arguments and corresponding roles in the event in a generated way.
Transformer-ban based pre-trained language model con- sisting both an Encoder and Decoder.
Following previous works (Ma et al.. 2022), we feed the context into BARI-Enccder and the role into BART-Decoder separately. Specifically, given the current trigger word zt, first de- fine the trigger markers (t) and (/t) as special to- kens and insert them into context before and after the trigger word æf to create an input con- text X. In the next step, BART-Encoder is employed to encode the input context X to obtain the sequence representation 11 Fi- nally, benefiting from mutual interaction informa- tion at the cross-attention layers in the decoder module, the BART-Decoder is applied to learn richer representations for the context and roles, turning H₂ BART-Decoder(HFF Hene, Hgre) and H, BART- BART-Decoder(R':: Homo)) for the context and roles, where R' denotes connecting different roles r; with learnable, role-specific pseudo tokens, H, denotes the event-oriented context representa- tion, H, (hr,, hrg} denotes context-oriented role representation, and d denotes the maximum number of roles for this event!.
3 Methodology
In this section, we first formulate the EAE task, then introduce the overall framework of our model, illustrated in Figure 2, which contains four core components: Role-Aware Encoding, Bi- Granularity Dependency Construction, Intra2Inter Dependency Interaction, and Dependency-Awi -ware Argument Extraction.
Task Definition We first describe the task for- malization of EAE. Formally, given context X = { In) of n words, the event trigger word xf X, the event type t T, where T= (t,,...,tu) is the set of all u different event types and the set of epent-specific role types R'. The EAE task aims to extract all the arguments {a1,..., aa} related to x² in X, and assign a role r E Rf to each extracted argument a,, where og is a text span in X.
3.2 Bi-Granularity Dependency Construction
The dependency construction module constructs two granularities of dependencies: intra-event de- pendency and inter-event dependency.
3.1 Role-Aware Encoding
Given context X and the set of event-specific role types R this module generates event-oriented context representation and context-oriented role representation. We use pre-trained BART (Lewis et al., 2020) as our encoding model, a standard
Intra-Event Dependency Considering the one- to-many form of trigger words and arguments in the
'The number of each role is determined by the maximum number of arguments for this role in the training set.

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event structure, the roles participating in the same event are closely related, so the intra-event graph representing the dependency within the event is a fully connected graph. For each event type f, the nodes V/ C Vi in the event are context-oriented role representation lr, E Hr, using the co-occurrence information of roles of event type in the training set as dependency weight:
Afterward, we use the intra-event graphs of the current event me and the most similar event m, construct the inter-event graph. Specifically, the nodes Vg of the inter-event graph are the roles of multiple event types, which are obtained by map- ping the corresponding nodes in the intra-cvent graphs of the current event and the most similar event. The dependency weights of the inter-event graph are constructed from the training set based on the co-occurrence information of all roles:
aty count(t, re, Ty)
count(l, rz)
count Ty)
Bij
(4)
Finally, after normalized symmetric (Chung, 1997) À AD- where D denotes degree matrix. The intra-event graph with weight dy is used to enhance the representations for hG, via a Graph Convolutional Network (GCN) (Kipf and Welling, 2017) with K layers:
count(r:)
Different from the intra-event graph, the inter- event graph captures dependeneies between events, so its nodes and edges correspond multiple event types and roles, not just single event type. Fi- nally, the representation of the inter-event graph is learned using a GCN similar to Equation 2.
* 2 = ReLU Σ aljeft +b)
(2)
ByEV
3.3
Intra2Inter Dependency Interaction
where w and b are learnable parameters, and sf represents the role representation for on at the k-th layer of GCN (2) hr,)
We use two different GCNs to learn the represen- tation of the intra-event graph and the inter-event graph. To propagate dependency information at different granularities, we align nodes representing the same roles in the intra-event and inter-event graph at each layer of the GCN: Z Z ( /2, where Z and Z represent the node representation of the intra-event and inter-event graph at the k-th layer of the GCN, respectively. The dependency representations are obtained by the above two K-layer GCNs and K times of depen- dency interaction, and finally the dependency infor- mation is assigned to the context representations by concatenating: H FFN(concat( H,, )))), where FFN is a feed forward network.
Inter-Event Dependency Different from intra- event dependencies, inter-event dependencies are represented by the inter-event graph to construct role dependency information between different events. In order to model the role dependency in- formation between different events, we propose memory unit M to store all the intra-event graphs in the form of tensors, which are dynamically up- dated according the corresponding indexes at each training epoch. The intra-event graph obtained by the retrieval module will be used to construct the inter-event graph, and the dependencies between different events will be constructed. Specifically. we first use cosine similarity in the retrieval module to retrieve the intra-event graph of the event most similar the current event:
Event Structure Constraint To learn and model the event structure information, we add two ad- ditional event structure constraints: the rolc con- straint, which uses the nodes of the dependency graph predict the role type, and the event straint, which uses the entire dependency graph predict the event type:
S(m:/ mm)) = f(Hr)) f(Hr.)
|If(H,))I (H,(H,))I
m. arg max (S(m))m)))) m.EM
C- -- Σ
log (Softtax((x) (K,, w²))
XED Nr EH
where represents the mean pooling operation, m. and m, denote the current event and the most similar event, respectively. my E M denotes the event in the memory unit, and the corresponding H,, and H,; represent the context-oriented role representation of the current event and the event in the memory unit respectively.
C. - Σ - (an(SSomax((((H))r))
(5)
XED
where w" and 2h are learnable parameters, C, and C. represent role constraint loss and event con- straint loss respectively, and ranges over all con- text in the dataset.

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3.4 Dependency-Aware Argument Extraction Given the context representation Hg and the set aapendenccyawarre role representation Hy, each Nr, aims to extract the corresponding argument span (sk, ex)) from the context X, where and e are the start and end word indices of the argument. The span (8k;C8) will be set to (0, 0) if Mi, has no corresponding argument (the current event has no argument about the role, or the argument-slot number of this role exceeds the actual argument number). For each N'u we calculate the distribu- tion of each token being selected as the start and end position of the argument (Du and Cardie, 2020; Ma et al., 2022):
Experiments
4.1 Experimental Setup
Datasets We conduct experiments on three commonly used EAE datasets: RAMS (Ebner et al., 2020), WikiEvents (Li et al., 2021) and ACE05 (Doddington et al., 2004). RAMS and WikiEvents are widely used document-level EAE datasets, and ACE05 is another classic dataset for sentence-level EAE tasks. Detailed statisties are listed in Appendix A.
We measure the perfor- mance with three evaluation metrics: (1) Argu- ment Identification (Arg-I): an event argument is conrectly identified if its offsets and event type match those of any of the argument mentions. (2) A Argument Classification (Arg-C): an event argu- ment correctly classified if its role type is also correct. (3) For WikiEvents dataset, we follow pre- vious work (Li et al., 2021), additionally evaluate Argument Head Classification (Head-C), which only concerns the matching of the headword of an argument. The F-measure (F1) score is used to evaluate the performance of the model.
Evaluation Metrics
logit (h/ O w E R" logit (h/r @ w H2 R=" (6)
where stort and and are learnable parameters, and @ represents the element-wist multiplication.
We calculate probabilities where the start and end positions are located as follows:
start Pk Softmax(logit, E RR"
pend Softmax (logitx end) R
Pk
The argument extraction loss is defined as:
Implementation Details We initialize our mod- els with the pre-trained BART base models (Lewis et al., 2020). For important hyper-parameter and details, plcase refer to Appendix B.
(x( = (log Pk
start (sk) + log pf (ex))
La = Σ (X)
XCD 1=1
Baselines
We compare our model to several rep- resentative and competitive baselines:
The overall loss function is divided into three parts: argument extraction loss La, role constraint
EEQA (Du and Cardie, 2020) treats sentence- level EAE as a QA task, and obtains the start and end offsets of the argument spans through question answering.
loss C,, and event constraint loss f.:
L= La Xn,, X2Co
and \1, A2 are dyper-parameters
FEAE (Wei et al., 2021) is QA-based method extended EAE by considering argu- ment interactions via knowledge distillation.
3.5 Inference
During inference, we consider all candidate spans of the argument C {ii,)) E n²,0 j i < 5) ((0,0) 0)), ensuring that the length of all spans does exceed the threshold 8 and (0, 0) means there is no argument. The argument of each Mr extracted by enumerating and scoring all candidate spans as:
DocMRC (Liu et al., 2021) is another QA- based method, assisted by two data augmenta- tion regimes.
BART-Gen (Li et al., 2021) defines EAE as a aequence-to-sequene task and generates cor- responding arguments in predefined format.
score; logit stgart (i) + logitg"d (10)
The span with the highest score will be selected as the prediction result. Specifically, the prediction span of 14, is computed:
PAIE (Ma al., 2022) is a prompt-based method. It extracts argument spans by con- structing role templates and is the current SOTA model. For fair comparison with our model, we use its soft prompt setting.
($), éx)
arg max sc.
(11)
(C

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ACE05
RAMS
WikiEvents
Model
PLM
Arg-I
Arg-C
Arg-I
Arg-C
Arg-I
Arg-C
Head-C
FEАE (Wei et al., 2021)
BERT
53.5
47.4
DocMRC (Liu et al., 2021)
BERT
45.7
43.3
EEQA (Du and Cardie, 2020)
BERT
68.2
65.4
46.4
44.0
54.3
53.2
56.9
BART-Gen (Li et al., 2021)!
BART
59.6
55.0
50.9
44.9
47.5
41.7
44.2
EEQA-BART (Ma et al., 2022)1
BART
69.6
67.7
49.4
46.3
60.3
57.1
61.4
PAIE (Ma et al., 2022)
BART
74.3
70.1
53.9
48.0
66.2
60.1
63.8
EDGE (Ours)
BART
75.3
70.6
55.2
49.7
68.2
62.8
65.9
Table Event argument extraction results on three mainstream datasets. Both BERT and BART here are base-scale. Result marked are from (Ma al., 2022). The best results are marked bold font.
Model
Arg-I
Arg-C
EDGE
EDGE
55.2
49.7
242
w/o inter-event dependency
54.0
47.8
40
w/o retrieval module
54.7
48.5
38
w/o role constraint
55.1
48.9
Number
Argument Number
w/o event constraint
55.0
49.4
(a) Same-Argument Role
(b) Same-Role Argument
Table 2: Ablation study results on RAMS.
Figure Experimental results of argument- role assign- ment. (a). Different numbers of roles for an argument on RAMS. (b). Different numbers of arguments for a role WikiEvents.
4.2 Overall Performance
Table 1 compares method with all baselines the ACEOS, RAMS, and WikiEvents datasets. We have several observations and discussions:
our model not only achieves better results on all evaluation metrics but also achieves higher perfor- mance gains on the more difficult Arg-C metric of document-leve EAE. This result shows that sim- ply implicitly constructing dependencies between argument roles is not sufficient for EAE. By explic- itly constructing dependencies between roles, model can more accurately identify and extract the corresponding arguments.
(1) Our method consistently outperforms the latest baselines and achieves the SOTA perfor- mance. Specifically, our method achieves 75.3 Arg-I F1 and 70.6 Arg-C Fl ACE05. On the RAMS dataset, Our method improves 1.3-8.8 Arg- FI and 1.7-5.7 Arg-C Fl over previous meth- ods. On the WikiEvents dataset, our method im- proves 2. 2.0-20.7 Arg-1 F1, 2.7-21.1 Arg-C I'l and 2.1-21.7 Head-C FI. These results demonstrate that our method is superior to previous methods in both argument identification and classification.
4.3 Ablation Study
We conduct ablation studies to investigate the ef- fectiveness of each component of our approach and the results are shown in Table 2. Specifi- cally, "w/o inter-event dependency" means remov- ing inter-event dependencies quantify its effec- tiveness, "w/o retrieval module" means removing the retrieval module and using only current events for graph representation, "w/o role constraint" and "w/o event constraint" mean that the two auxiliary constraints are not considered respectively.
(2) We find that extracting all arguments from an event end-to-end is more efficient than extracting arguments one at a time. Compared with EEQA, the end-to-end methods (our method and PAIE) achieve different degrees of performance improve- ment, which demonstrates the existence of rich im- plicit dependency information between roles. Inde- pendent role-specific QA methods can only extract argument time, ignore this dependency information, and still struggle with the task of ar- gument identification and classification.
We can find that each module can help boost the performance of EAE, removing any module will lead to different degrees performance degrada-
(3) Compared to the current SOTA model PAIE,

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70-
PAIE
50-
0
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Cummury
antecc
transporto
bonunclar War
ergu
targut
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Instrument
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prevonter
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monuy
werkko
detainoo
prosocutor
Color
Figure Experimental results for each argument role on RAMS (the argument number 10). The ordering of roles is from the largest to the smallest number of corresponding arguments.
model in this scenario, we split the data according to the number of roles corresponding to the argu- ment. Figure 3(a) shows the experimental results in scenarios with different numbers of roles. Com- pared with the baseline, our method achieves better results in all scenarios and exhibits better robust- ness. It worth noting that our method achieves greater performance improvement the number of roles corresponding to arguments increases, which indicates the necessity of building dependencics among the roles in such scenarios.
(a) wlo Dep
(b) EDGE
Figure 5: 2D visualization of projected role represen-
tation. (a). Role representation trained by w/o Dep (EDGE without any role dependency). (b). Role repre- sentation trained by EDGE.
5.2 Same-Role Argument Assignment
In addition to one argument playing multiple roles, there may be multiple arguments playing the same role in complex event. These arguments are indis- pensable in the event, is important extract them to understand the complex event. In order to explore the performance of our method this scenario, we split the data according to the num- ber of arguments corresponding to the role, and the experimental results are shown in Figure 3(b). Our method achieves better performance than the baseline in all scenarios, especially in the most complex scenarios where the number of arguments no less than three, achieving 8.0 Arg-C Fl gains, This demonstrates that our method can alleviate the argument and role assignment and more accurately extract multiple arguments for the same role by building intra-event and inter-event dependencies to learn semantic information between roles.
tion. To be specific, (1) Without inter-event de- pendency, the performance drops dramatically by about 1.9 points in terms of F1. This justifies that it is necessary to build dependencies between events and establish connections between different events. (2) Removing the retrieval module has performance drop of 1.2 Arg-CF11 which indicates that our retrieval module can improve task perfor- mance by efficiently retrieving similar events. (3) Similar observations can be found in other modules, removing the role constraint and event constraint results in different degrees of performance degra- dation, indicating that the event structure is helpful for argument identification and classification.
5 Analysis on Real Scenario
5.1 Same-Argument Role Assignment
5.3 Role Performance Analysis
In the EAE task, complete event usually includes multiple arguments and corresponding roles. Fur- thermore, an argument may play more than one role in an event, and extracting these arguments is more important for event understanding, but also more difficult. order to explore the performance of our
To examine the performance on the role of the long- tail distribution (Lbner al., 2020), we calculate the Arg-C Fi of the method on each role. The experimental results are shown in Figure 4. Com- pared with the PAIE, our method achieves the same

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Damascus reacted harshly to <t> bombing </t> of Kurdish militiaSunge in northern Syriitnee on Thursday morning by Turkey 's air force suacues vowing to intervene next time Ankara sends its planes over its border. Read more In a statement, the Syrian Defense Ministry accused Turkey of "flagrant aggression, which targeted innocent citizens," saying that it considers it dangerous development that could escalate the situation."
Input
Attacker
Target
Instrument
Place
w/o Dep
Turkey air force v
Kurdish militias
air force
northern Syria
Ukraine authorities said Moutaux' aimed to blow up Muslim mosque, a Jewish synagogue, tax collection organ- isations, police patrol units and numerous other locations' French regional newspaper L'Est Republicain identified the Gregoire Moulaux and said investigators raided his home (pictured) in Nant-le-Petil near the eastem city of Nancy late May. managed murder 130 people, with suicide bombers stucker <p> exploding </t> their
EDGE
stuckr devices around the Stade de France murget during football friendly between France and Germany...
Atlacker
Target
Instrument
Place
Turkey air force V
Kurdish militias
bombing
northern Syria
The case study of our proposed EDGE and w/o Dep. The event triggers are included in special tokens <t> and </t>, and the event arguments are shown in underline, with their roles in subscripts. In our method EDGE, the most similar event obtained by the retrieval module is additionally shown.
Figure 6:
or better results on 71.43% (20/28) roles. Specifi- cally, our method achieves performance improve- ment of more than 10 Arg-C F1 on multiple roles, such as instrument (15.79), origin (13.17), and em- ployee (11.11). All these observations verify that our method significantly improves the extraction performance of different argument roles by con- structing intra-event and inter-event role dependen- cies, which helps alleviate the long-tail problem.
from EDGE. Specifically, EDGE can accurately ex- tract all the arguments, while w/o Dep fails. At the same time, the input documents and retrieved events are armed attacks that cause citizens be injured, indicating that our retrieval module can accurately retrieve similar events and construct the inter-event role dependencies hetween these events. The results show that our method can retrieve simi- lar events with similar semantics and event struc- ture as the input document, and all arguments in events can be accurately extracted by constructing role dependencies between events.
5.4 Visualization Analysis
In order to explore whether our method fully mod- els the semantic information of argument roles, we map all roles on the RAMS development set to the 2-dimensional vector space through the t- Distributed Stochastic Neighbor Embedding (t- SNE) (Van der Maaten and Hinton, 2008) method for visual analysis. The visualization results are shown in Figure 5, where different roles are dis- played in different colors. Compared with w/o Dep, our method can cluster the roles of similar semantics together according to the semantic infor- mation of the context, showing better intra-role and inter-role distance under different events. This in- dicates that by constructing dependencies between argument roles and event structure constraints, our method can better learn the semantic information of roles in different events.
6 Conclusion
In this paper, we propose novel method based on role dependencies for EAE. Specifically, we first utilize the intra-event graph and the inter-event graph retrieved from the intra-event graph to au- tomatically construct intra-event and inter-event dependencies, and then fully learn and model the dependencies of roles in different scenarios through two different GCNs. To further optimize depen- dency information and event representation, we propose dependency interaction module and two auxiliary tasks to improve the argument extraction ability of the model different scenarios. We con- duct extensive experiments on three EAE datasets and compare our model with several strong base- lines. The results show that our model achieves SOTA performance and outperforms all basclines in different scenarios. In further work, we are inter- ested in constructing dependency information for other information extraction tasks and would like to apply our model to these tasks, such as named entity recognition and relation extraction.
5.5
Case Study
In this subsection, we show example from RAMS dataset to illustrate the capability of our method. The example is presented Figure 6, including the input document, event argument ex- traction results, and the most similar event retrieved

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Limitations
Weischedel. 2004. The automatic content extrac- tion (ACE) program - tasks, data, and evaluation. In Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC'04) '04) Lisbon, Portugal. European Language Resources As- sociation (ELRA).
We discuss the limitations of our research as fol- lows:
Our method uses additional graph convolu- tional networks to construct the dependency of argument roles, resulting in increase in training parameters, and one training process occupies nearly one NVIDIA V100 32GB GPU.
Xinya Du and Claire Cardie. 2020. Event extraction by answering (almost) natural questions. Proceedings of the 2020 Conference on Empirical Methods in Nat- ural Language Processing (EMNLP), pages 671-683, Online. Association for Computational Linguistics.
Xinya Du, Sha Li, and Heng Ji. 2022. Dynamic global memory for document-lever argument extraction. In Proceedings of the 60th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 5264-5275, Dublin, Ireland. As- sociation for Computational Linguistics.
Our method requires event ontology informa- tion to construct intra-event and inter-event graphs, and not suitable for scenarios where event ontology is unknown.
Acknowledgements
Seth Ebner, Patrick Xia, Ryan Culkin, Kyle Rawlins, and Benjamin Van Durme. 2020. Multi-senience ar- gument linking. In Proceedings of the 58th Annual Meeting of the Association for Computational Lin- guistics, pages 8057-8000 8077-0077707 Online. Association for Computational Linguistics.
This work is supported by the National Key Research and Development Program of China AN0.2022YF33102200 and Strategic Priority Re- search Program of the Chinese Academy of Sci- ences with No. XDC02030400.
I-Hung Hsu, Kuan-Hao Huang. Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, and Nanyun Peng. 2022. DEGREE: A data-efficient generation- basedd event extraction model. Pro- ceedings of the 2022 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1890-1908, Seattle, United States. Association for Computational Linguistics.
References
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RNN and tensor- based
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Yaojie Lu, Hongyu Lin, Jin Xu, Xianpei Han, Jialong Tang, Annan Li, Le Sun, Meng Liao, and Shavyi Chen. 2021. Text2Event: Controllable sequence-to- structure generation for end-to-end event extraction.
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David Wadden, Ulme Wennberg, Luan, and Han- naneh Hajishirzi. 2019. Entity, relation, and event extraction with contextualized span representations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference Natural Lan- guage Processing (EMNLP-IJCNIIP), pages 5784- 5789, Hong Kong. China. Association for Computa- tional Linguistics.
A Dataset statisties
RAMS focuses on a document-level EAE task, in- cluding 139 event types and 65 argument roles, with more than 9k events. WikiEvents is another widely used document-level EAE dataset, which contains 50 event types and 59 argument roles, con- taining more than 3.9k events. ACE05 is a sentence- level extraction dataset, including 33 event types and 35 argument roles, with more than 5k events. We follow the official data split of each dataset, For ACE05, we follow the pre-processing procedure of DyGlE++ (Wadden et al.. 2019). The specific statistics of the datasets are listed in Table 3 and 4.
Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu,, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: Contrastive Pre-training for Event Extraction. In Proceedings of the 59th Annual Meet- ing of the Association for Computational Linguistics and the 11th International Joint Conference on Natu- ral Language Processing (Volume I: Long Pupers), pages 6283 6297. Online. Association for Computa- tional Linguisties.
Dataset
#Split
#Doc
#Event
#Argument
Train 3,194
7,329
17,026
Kaiwen Wei, Xian Sun, Zequn Zhang. Jingyuan Zhang, Guo Zhi, and Li Jin. 2021. Trigger is not sufficient: Exploiting frame- aware knowledge for implicit event argument extraction. In Proceedings of the 59th An- nual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer- ence on Natural Language Processing (Volume I: Long Papers), pages 4672 4682, Online. Association for Computational Linguistics.
RAMS
Dev
399
924
2,188
Test
400
871
2,023
Train
206
3,241
4,542
WikiEvents
Dev 20
345
428
Test 20
365
566
Table 3: Data statistics of RAMS and WikiEvents.
Runxin Xu, Tianyu Liu, Lei Li, and Baobao Chang. 2021. Document-leve event extraction via heteroge- neous graph-based interaction model with a tracker.
Dataset
#Split
#Sent
#Event
#Argument
Train 17,172
4,202
4,859
In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the
ACEOS
Dev
923
450
605
Test
832
403
576
1 11th International Joint Conference on Natural Lan- guage Processing (Volume 1: Long Papers), pages 3533-3546, Online. Association for Computational Linguistics.
Table 4: Data statistics of ACT10..
Runxin Xu, Peiyi Wang, Tianyu Liu, Shuang Zeng,
B Implementation Details
Baobao Chang, and Zhifang Sui. 2022. A two-stream AMR-enhanced model for document-leve event ar- gument extraction.
the HuggingFace's Transformers library to implement the BART basc model. The model is optimized with the Adam optimizer (Loshchilov and Hutter, 2019). Other important
In our implementations,
Proceedings of the 2022 Con- ference of the North American Chapter of the As- sociation for Computational Linguisties: Human Language Technologies, pages 5025-5036, Seattle, United States. Association for Computational Lin- guistics.
are shown in Table 5.
soper--prrametr
Value
Sen Yang, Dawei Feng, Linbo Qiao, Zhigang Kan, and Dongsheng Li. 2019. Exploring pre-trained language models for event extraction and generation. In Pro- ceedings of the 57th Annual Meeting of the Asxo- ciationfor Computational Linguistics, pages 5284- 5294, Florence, Italy. Association for Computational Linguistics.
Training epochs Learning rate
10
2e-5 (RAMS) 3e-5 (Other)
Layers of GCN Max span length
2
8
Window size
250
Max encoder seq length Max decoder seq length X
500 / 192 (ACE05)
80
Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma. and Eduard Hovy. 2020. A two-step approach for implicit event argument detection, In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7479-7485, Online. Association for Computational Linguistics.
0.2
X2
0.5
Table 5: Hapeer-arameters for EDGE.


Reviews:
[{"review_id": "XduGtNemm5", "reviewer": "Reviewer_N1pm", "summary": "", "questions": "1. The whole structure of the module is not clear in Fig. 2. Only inter and intra event graphs are presented.\n2. The inter and intra event graphs are constructed using the trigger and event type information, especially for Inter-event Dependency, why applying similarity between the events, which may not contain useful information.\n3. In ablation study, only inter-event dependency is removed, however, in the method, inter and intra are bond with each other, the 1.9 points drop after removing inter-event maynot demonstrate the effect of only inter-event dependency. Why not remove intra-event dependency as well?\n4. The idea of construct graphs also shows in Lv et al.  2021 (HGEED: Hierarchical graph enhanced event detection), which constructs sentence-level and document level graphs. Wha's the difference? Or it's just the same idea applying to different tasks.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank all reviewers for their time and for giving valuable and supportive comments. We will take these comments into consideration and make the necessary revisions. \n\nQuestion 1: The whole structure of the module is not clear in Fig.2.\n\nIn Fig.2, we show the overall framework of our method. Specifically, we use a specific example to show the whole process, and mark the text description under the specific modules, such as input module, dependency building module, dependency interaction module and argument extraction module. At the same time, we delineate the real similar and dissimilar events in the memory unit. More often, we also use different colors to distinguish argument roles, use solid lines with arrows to indicate the flow of modules, and use dotted lines to indicate dependent interactions. Finally, the specific extraction results of this example are depicted in the extraction module.\n\nQuestion 2: Why applying similarity between the events, which may not contain useful information.\n\nWe apply the similarity between events to retrieve similar events, hoping that the similar event structure and context can provide more information to the current context and help the current extraction. More, our graph assigns different weights to the argument roles, and we have carried out dependency interaction between GCNs in each layer, which can perform information interaction at different granularities and filter redundant information.\n\nQuestion 3: Why not remove intra-event dependency as well?\n\nIntra-event dependencies are the basis of inter-event dependencies, so we need to first build and store the intra-event dependency graph, and then build the inter-event dependency graph.\n\nQuestion 4: Same idea Lv et al. 2021 (HGEED: Hierarchical graph enhanced event detection)\n\nHGEED and our method have completely different motivations. HGEED aims to use documents to construct dependency information between sentences, so they build sentence-level and document-level graphs. However, our starting point is to construct the dependency of argument roles within the same event and between similar events, so we construct intra-event and inter-event graphs using event structure as the basic unit, which is not limited to the current context.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Experimental results on the ACE05, RAMS, and WikiEvents datasets show the great advantages of the proposed approach.\n2. Intra-event and inter-event dependency-aware graph network are presented for EAE,  learning rich role semantic information without using manual label templates.", "reasons_to_reject": "1. The whole structure of the module is not clear in Fig. 2. Only inter and intra event graphs are presented.\n2. The inter and intra event graphs are constructed using the trigger and event type information, especially for Inter-event Dependency, why applying similarity between the events, which may not contain useful information.\n3. In ablation study, only inter-event dependency is removed, however, in the method, inter and intra are bond with each other, the 1.9 points drop after removing inter-event maynot demonstrate the effect of only inter-event dependency. Why not remove intra-event dependency as well?\n4. The idea of construct graphs also shows in Lv et al.  2021 (HGEED: Hierarchical graph enhanced event detection), which constructs sentence-level and document level graphs. Wha's the difference? Or it's just the same idea applying to different tasks.", "questions_to_authors": "1. The whole structure of the module is not clear in Fig. 2. Only inter and intra event graphs are presented.\n2. The inter and intra event graphs are constructed using the trigger and event type information, especially for Inter-event Dependency, why applying similarity between the events, which may not contain useful information.\n3. In ablation study, only inter-event dependency is removed, however, in the method, inter and intra are bond with each other, the 1.9 points drop after removing inter-event maynot demonstrate the effect of only inter-event dependency. Why not remove intra-event dependency as well?\n4. The idea of construct graphs also shows in Lv et al.  2021 (HGEED: Hierarchical graph enhanced event detection), which constructs sentence-level and document level graphs. Wha's the difference? Or it's just the same idea applying to different tasks.", "paper_topic_and_main_contributions": "This paper proposed a perspective to automatically construct intra-event and inter-event argument role dependencies, learning the interactions between different roles.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Lv et al.  2021 (HGEED: Hierarchical graph enhanced event detection)"}, "extra_text": {}}, {"review_id": "N4FgODWXNC", "reviewer": "Reviewer_Y77w", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank all reviewers for their time and for giving valuable and supportive comments. We will take these comments into consideration and make the necessary revisions.\n\nQuestion 1: Performance gains are relatively small.\n\nWe conduct experiments on two document-level and one sentence-level event argument extraction datasets. Compared with multiple baselines, we achieve $1.7\\sim5.7$, $2.7\\sim21.1$, and $0.5\\sim15.6$ point performance improvements on the strict Arg-C F1 metric, respectively. This demonstrates the effectiveness and generalization ability of our method.\n\nQuestion 2: The long-range trigger-arguments are not fully convincing.\n\nOur method focuses on building intra-event and inter-event dependencies, and to explore whether other dependencies can be captured, we additionally conduct long-range trigger-arguments experiments. Even though our method does not have any modules to capture long-distance dependencies, performance gains are still achieved at multiple distances.\n\nQuestion 3: Variance of the scores.\n\nWe experiment with multiple seeds and report the average performance on the test set. We will add specific experimental details in the appendix.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Pos:\n\n* The idea of constructing intra-event and inter-event dependency graphs is creative, intuitive, and well-motivated. Encoding structured knowledge about event roles into graphical networks is an elegant approach.\n\n* Incorporating an event memory and retrieval module is innovative, allowing inter-event dependencies to be dynamically constructed at inference time.", "reasons_to_reject": "Neg:\n\n* The performance gains over prior methods appear relatively small based on the results presented. \n\n* The authors make a compelling case that long-range trigger-argument dependencies are captured, but the results in Table 3 are not fully convincing. \n\n* The results should reflect the variance of the scores from multiple experiments with different seeds.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": " The authors propose an interesting approach to modeling intra-event and inter-event dependencies for event argument extraction. The idea of constructing graphical networks to capture role relationships within and across events is novel and intuitively appealing. ", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "J2rZJMjd30", "reviewer": "Reviewer_C7eU", "summary": "", "questions": "See reasons to reject.", "limitations": "", "rating": 3, "confidence": 3, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank all reviewers for their time and for giving valuable and supportive comments. We will take these comments into consideration and make the necessary revisions.\n\nQuestion 1: It is also not clear how these two parameters are associated with the architecture of BART decoder.\n\nThe two parameters of the Decoder represent input ids and encoder hidden states respectively. In line 207, $H_x^{(enc)} = {Encoder}(\\tilde{X})$ and $H_x = {Decoder}(H_x^{(enc)};H_x^{(enc)})$ represent a completed Encoder-Decoder process, and there is no need to repeatedly encode the input ids, so the two parameters of Decoder in $H_x = {Decoder}(H_x^{(enc)};H_x^{(enc)})$ are both $H_x^{(enc)}$. For $H_{r} = {Decoder}(\\tilde{\\mathcal{R}}^{t}; H_x^{(enc)})$, it represents a separate decoding process, so the two parameters are $\\tilde{\\mathcal{R}}^{t}$ and $H_x^{(enc)}$ respectively. \n\nQuestion 2: How to choose or compute $H_r$ when there are multiple triggers?\n\nFor the event argument extraction task, the trigger word and event type in the current context will be given. Following the classic work (Ma et al., 2022), we segment the context according to the window centered on the trigger word (the window length is set in the appendix, consistent with previous work), which not only guarantees the input length limit of the pre-trained model, but also divides a small number of contexts with multiple trigger words into a single event, which reduces the difficulty of extraction and improves task performance.\n\nQuestion 3: How to store those representations and applied to unseen contextual is also unclear.\n\nWe propose a memory unit $M$ to store all intra-event graphs during the training phase. Specifically, we save the id of the intra-event graphs and store the intra-event graphs in the form of tensors (gradients removed). In each training epoch, we will lookup the saved tensors by id and update it.\n\nQuestion 4: $H_{r_i}$ and $H_{r_c}$ are introduced without further clarification.\n\nWe explained in line 256 that $m_c$ represents the current event, $m_i \\in M$  represents the event in the memory unit, and the corresponding $H_{r_c}$ and $H_{r_i}$ represent the context-oriented role representation of the current event and the event in the memory unit respectively.\n\nQuestion 5: Previous work \"neglects to build dependency information among argument roles\", clear comparisons and discussions on this line of research.\n\nWe use \"mostly\" and \"usually\" in the paper. In event-related tasks (not just event argument extraction), previous work (e.g. (Lin et al., 2020; Cui et al., 2020; Lv et al., 2021; Xu et al., 2021)) mostly builds dependencies between entities, instances, or sentences. However, we innovatively use the event structure as a fundamental unit to build the dependencies of the argument roles. Our advantage is that it conforms to the definition of the event structure and does not require a lot of pre-processing work. At the same time, taking the event as a unit can also help us retrieve meaningful similar events and complete the argument extraction of the current event.\n\nReference\n\n[Lin et al., 2020] A Joint Neural Model for Information Extraction with Global Features\n\nThey explicitly model cross-subtask and cross-instance inter-dependencies.\n\n[Cui et al., 2020] Edge-Enhanced Graph Convolution Networks for\nEvent Detection with Syntactic Relation\n\nThey integrate syntactic structure and typed dependency labels to improve neural event detection.\n\n[Lv et al., 2021] HGEED: Hierarchical graph enhanced event detection\n\nThey aim to use the information between sentences in the document to build dependencies.\n\n[Xu et al., 2021] Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker\n\nThey focus on the global interactions among sentences and entity mentions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The motivation for using GCN to model the argument dependency is clear.\n\n2. The experimental results show promising state-of-the-art performance, indicating the effectiveness of their proposed method.", "reasons_to_reject": "1. My major concern with this paper is the unclear writing. There are many technical details that are not explained properly, making it difficult to understand the architecture and reproduce this work. Below are a few examples:\n\n- In Line 207, the decoder input parameters are not quite confusing. The first parameter can be a list of vectors ($H_x = Decoder(H_X^{enc}; H_X^{enc})$ or a list of tokens ($H_r = Decoder( \\hat{\\mathcal{R}}^t, H_x^{(enc)})$). It is also not clear how these two parameters are associated with the architecture of BART decoder.\n- In the description of intra-event dependency (starting from Line 216), they introduce an intra-event graph structure related to each event type, and the graph is initialized with role representations $H_r$. However, in Line 207, we know that $H_r$ is not only a role-dependent representation, but is also trigger-depended ($\\hat{X}$ introduces special tags, $\\langle t \\rangle$ $\\langle /t \\rangle$ for trigger). And in the description in Line 197, we know that there is only one trigger word marked in $\\hat{X}$. Therefore, it is unclear how to choose or compute $H_r$ when there are multiple triggers (not co-referential) that have the same event type.\n- In Line 245, they mention that all intra-event graphs are stored and updated at each training epoch. But as in the previous discussion, intra-graph representations are contextual-dependent. How to store those representations and applied to unseen contextual is also unclear.\n- In Line 253, $H_{r_i}$ and $H_{r_c}$ are introduced without further clarification.\n\nI would strongly suggest the authors carefully reorganize and polish the descriptions in this paper.\n\n2. Another concern is that this paper claims that previous work \"neglects to build dependency information among argument roles\". But there is indeed some work discussing the dependencies. One paper I can remember is [Lin et al., 2020]. Probably there are some older ones with integer linear programming. I would recommend the authors make clear comparisons and discussions on this line of research.\n\n**Reference**\n\n[Lin et al., 2020] A Joint Neural Model for Information Extraction with Global Features", "questions_to_authors": "See reasons to reject.", "paper_topic_and_main_contributions": "This work presents an event argument extraction model that models the intra and inter-event argument dependency with graph neural networks. This work argues that different arguments within an event or between similar events are highly depended, and the dependency can be modeled by the proposed architecture. The authors conduct experiments on three datasets, ACE05, RAMS, and WikiEvents, and find that their proposed methods can outperform several baselines.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "See reasons to reject.", "Typos_Grammar_Style_and_Presentation_Improvements": "See reasons to reject."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper introduces a novel approach for automatically capturing dependencies between event arguments within and across events using graphical networks. Additionally, the incorporation of an event memory and retrieval module during inference adds innovation to the proposed architecture. The experimental results, evaluated on ACE05, RAMS, and WikiEvents datasets, demonstrate the significant advantages of this approach.

Two out of three reviews agree on the good soundness of the work; one reviewer raised concerns over the clarify of the presentation of technical details, which I think is clarified in authors' rebuttal. Overall, I don't see obvious improvement areas in terms of soundness. Reviews also point out that the idea of constructing intra-event and inter-event dependency graphs is novel and well-motivated; however, the performance gains over prior methods appear relatively small based on the results presented.
Decision: Accept-Findings