Venue: NAACL 2024

# Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction

Jing Xu1, Dandan Song1*, Siu Cheung Hui2, Zhijing Wu1, Meihunizi Jia1, Hao Wang1, Yanru Zhou1, Changzhi Zhou1, Ziyi Yang3

###### Abstract

In event argument extraction (EAE), a promising approach involves jointly encoding text and argument roles, and performing multiple token linking operations. This approach further falls into two categories. One extracts arguments within a single event, while the other attempts to extract arguments from multiple events simultaneously. However, the former lacks to leverage cross-event information and the latter requires touspher predictions with longer encoded role sequences and extra linking operations. In this paper, we design a novel separation-and-fusion paradigm to separately acquire cross-event information and fuse it into the argument extraction of a target event. Following the paradigm, we propose a novel multiple token linking model named Sep2F, which can effectively build event correlations via roles and preserve the simple linking predictions of single-event extraction. In particular, we employ one linking module to extract arguments for the target event and another to aggregate the role information of multiple events. More importantly, we propose a novel two-fold fusion module to ensure that the aggregated cross-event information serves EAE well. We evaluate our proposed model on sentence-level and document-level datasets, including ACE05, RAMS, WikiCvents and MLEE. The extensive experimental results indicate that our model outperforms the state-of-the-art EAE models on all the datasets.

1School of Computer Science and Technology, Beijing Institute of Technology, China

2Nanyang Technological University, Singapore

3School of Cyberspace Science and Technology, Beijing Institute of Technology, China {xuying,sdd,zhijingwu,jmhuizi24,wanghaobit,zhouyanru, zhou_changzhi97}@bit.edu.cn;asschui@ntu.edu.sg;yziyiyi@bit.edu.cn

## 1 Introduction

As a crucial step of event extraction (EE), event argument extraction (EAE) aims to recognize all arguments and their roles for each event in text. The recognized arguments can act as structured semantic information and greatly influence various downstream tasks Wen et al. (2021); Wu et al. (2022); Fung et al. (2023); Liu et al. (2023b). Despite the impressive advancements in large language models (LLMs) such as ChatGPT OpenAI (2022), the evaluations Han et al. (2023); Li et al. (2023a); Wei et al. (2023) indicate that EAE remains challenging.

Recently, significant improvements have been made in EAE using prompt-based methods by extractive Ma et al. (2022); He et al. (2023); Nguyen et al. (2023); Li et al. (2023b) and generative Hsu et al. (2022); Du et al. (2022); Zhang et al. (2023) styles. However, the former is limited by pre-determined numbers of repeated role slots when extracting multiple arguments with the same role, while the latter is weak in accommodating long-distance argument extraction. Besides, most of their performance relies on the quality of their designed prompts.

Different from the prompt-based methods, several works Wang et al. (2022); Lou et al. (2023); Liu et al. (2023a) concatenate input text and argument roles as a natural language sequence, jointly encode them, and perform multiple token linking operations for EAE or universal information extraction. The direct and parallel linking operations between

Figure 1: Different categories of multiple token linking models for EAE. _E1: Event 1. E2: Event 2._ The trigger words are highlighted in red, the concatenated roles are in yellow, and the arrows of different colors represent different linking operations. Note that we only exhibit one argument of each event for simplification.



all arguments and roles make extracting multiple arguments with the same role straightforward, and promote the interaction among scattered arguments within the long text. Additionally, they do not need well-designed prompts. Based on the number of events extracted at a time, we can further divide these methods into two categories: _single-event extraction_ and _multiple-event extraction_.

The first category of methods Wang et al. (2022); Liu et al. (2023) involves concatenating text and event-specific roles as input, followed by two linking operations for each event. As shown in Figure 1, the argument "_James McDade_" plays different roles in both the "_Life.Die_" and "_Conflict.Attack_" events and is extracted separately. Though _single-event extraction_ facilitates simple linking predictions, it ignores the significant correlations across different events Zeng et al. (2022); He et al. (2023); Li et al. (2023). The second category Lou et al. (2023) attempts to extract arguments of multiple events simultaneously. Nevertheless, two issues come with this _multiple-event extraction_. First, the roles concatenated with the input text are no longer of a single event but instead of all involved events, making it more challenging to choose correct roles for arguments via link predictions. Second, compared with _single-event extraction_, two extra linking operations are needed to determine which event (trigger) the extracted arguments and corresponding roles belong to. As a result, they may accumulate more errors during the prediction process. Figure 1 demonstrates that to extract the argument "_James McDade_" from both the "_Life.Die_" and "_Conflict.Attack_" events simultaneously, the extra trigger-argument and trigger-role linking operations are essential.

To leverage both merits of the above two categories, we design a novel separation-and-fusion paradigm by (1) separating the cross-event information acquisition and the EAE process and (2) fusing the acquired cross-event information into EAE. Therefore, the final EAE can simultaneously preserve the simple linking predictions of _single-event extraction_ and leverage the cross-event clues like _multiple-event extraction_. Figure 1 illustrates the paradigm. The middle part is separated from the EAE process and acquires cross-event information via trigger-role linkages. The upward and downward arrows denote the cross-event information fusion.

Following the paradigm, we propose a novel multiple token linking model with **Sep**arate acquisition of cross-event information and **Two**-fold **F**usion for EAE, named **Sep2F**. To separate the cross-event information acquisition and the argument extraction process, we design two multiple token linking modules. Specifically, we introduce one linking module to bridge each event trigger and its co-occurred roles for multiple events. Thus, the representations of different event triggers aggregate their co-occurred roles in parallel and provide critical cross-event information. Simultaneously, we employ another linking module to extract arguments for a target event. It performs two linking operations to obtain the argument spans and corresponding roles. More importantly, we propose a novel two-fold fusion module to effectively fuse the acquired cross-event information into the argument extraction for the target event. In details, we first dynamically fuse the text representations from the above two linking modules. Then, we utilize the fused text representations to obtain cross-module token linking scores. The linking scores are further fused into the final prediction scores. These two sequential fusions affect each other and deliver significant performance contributions. We summarize our main contributions as follows:

* We propose a novel separation-and-fusion paradigm for EAE. It can leverage cross-event information and retain the merits of single-event extraction simultaneously.
* Under the separation-and-fusion paradigm, we propose Sep2F, a novel multiple token linking model. Specifically, we design two linking modules to acquire cross-event information and extract arguments of a target event. Also, we introduce a two-fold fusion module to ensure that the acquired cross-event information serves the argument extraction well.
* We conduct extensive experiments on the widely used benchmarks, including ACE05, RAMS, WikiEvents and MLEE. Our proposed model outperforms the state-of-the-art EAE models by 2.0%, 1.0%, 2.8% and 2.5% in Arg-C F1, respectively.

## 2 Related Work

### Event Argument Extraction

Earlier EAE methods mainly fall into two classes: classification-based methods and MRC-based methods. The former treats entity mentions Wang 

et al., 2019; Ma et al., 2020; Xiangyu et al., 2021) or identified text spans (Ma et al., 2020; Ebner et al., 2020; Xu et al., 2022b) as argument candidates and employs randomly initialized classifiers to recognize argument roles. The latter designs question templates for argument roles and considers EAE as a machine reading comprehension problem Du and Cardie, 2020; Liu et al., 2020; Li et al., 2020; Wei et al., 2021). Lately, prompt-based methods have delivered impressive performance improvements. Specifically, the extractive ones locate role slots in prompts to mine prior knowledge from pre-trained language models for EAE Lin and Chen, 2021; Zhang et al., 2022; Ma et al., 2022; He et al., 2023; Li et al., 2023b). As for the generative ones, they leverage prompt templates and transformer-based encoder-decoder frameworks to extract the arguments within each event sequentially (Li et al., 2021; Hsu et al., 2022; Du et al., 2022; Zeng et al., 2022; Hsu et al., 2023; Zhang et al., 2023).

Most of the above methods ignore the correlations across different events and only a few works (Zeng et al., 2022; Du et al., 2022; He et al., 2023; Li et al., 2023b) consider the benefits from them. However, these methods are limited by the quality of prompts or degraded performance in long-range extraction. Therefore, this paper proposes a novel multiple token linking model that can capture cross-event information well and avoid these limitations.

### Multiple Token Linking

Recently, a rising interest has emerged in multiple token linking models for information extraction, which jointly encode text and task-specific labels as a natural language sequence and perform token linking operations. UniRel Tang et al. (2022) proposes entity-entity and entity-relation linking operations to extract relational triples. Wang et al. (2022) design a multiway attention mechanism to connect roles with argument candidates within a single event for EAE. RexUIE Liu et al. (2023a) uses different token linking operations to identify each event's argument spans and role types separately in the universal information extraction framework. Unlike RexUIE, USM Lou et al. (2023) employs extra trigger-argument and trigger-role linkings to determine which event the extracted arguments belong to for multiple-event extraction.

## 3 Proposed Model

We represent an instance as \((X,T,C)\), where \(X\) is the input text, \(T\) denotes the target event and \(C\) denotes the other events surrounding \(T\) within the text \(X\). Specifically, \(T\) is further represented as \((e,t,{\cal R}^{e})\), where \(e\) is the event type, \(t\) is the trigger word and \({\cal R}^{e}\) is the set of role types specific to \(e\). Similarly, \(C\) is represented as \(\{(\tilde{e}_{i},\tilde{t}_{i},{\cal R}^{\tilde{e}_{i}})|~{}i\leq|C|\}\) which provides the cross-event information. EAE aims to extract the argument set \({\cal A}\) for the target event \(T\), where each argument \(a^{(r)}\) in \({\cal A}\) is a snippet of the text \(X\) with the role type \(r\in{\cal R}^{e}\).

As illustrated in Figure 2, we introduce our proposed model Sep2F, which consists of three modules: Linking Construction for Multiple Events, Linking Construction for Target Event and Two-fold Fusion. Sep2F follows our designed separation-and-fusion paradigm. Specifically, the first two modules separate the acquisition of cross-event information and the argument extraction process of the target event. In contrast, the last module introduces our proposed fusion for the acquired cross-event information. Next, we describe these modules in details.

### Linking Construction for Multiple Events

In this module, we acquire cross-event information by aggregating the roles of multiple events, including the target event \(T\) and all surrounding events \(C\). To achieve the goal, we build the connections between different event triggers and their corresponding involved roles in parallel. First, we jointly encode text and all roles pre-defined in the given dataset. Then, we introduce the label matrix and score matrix for multiple trigger-role linkings within these events. Finally, we formulate the training loss in this module.

EncodingWe first verbalize each argument role as its role name, i.e., a single natural description word. For the few roles whose names contain multiple words, we employ additional special tokens to represent them. Note that the special token representations are not event-specific. Then, we concatenate all the verbalized roles pre-defined in the dataset as \(R_{M}\). After that, we jointly encode the sequence \(R_{M}\) and the input text \(X\) with a pre-trained language model (PLM) as follows:

\[\mathbf{E}^{M}=(\mathbf{h}_{1}^{M},\cdots,\mathbf{h}_{N}^{M},\mathbf{r}_{1}^{ M},\cdots,\mathbf{r}_{|R_{M}|}^{M})=\text{PLM}(X\oplus R_{M})\] (1) 

where \({\bf h}_{n}^{M}~{}(1\leq n\leq N)\) and \({\bf r}_{n}^{M}~{}(1\leq n\leq|R_{M}|)\) are the token embeddings within \(X\) and \(R_{M}\), respectively.

Label MatrixTo learn the respective role information for the multiple events, we design a label matrix \({\bf L}_{M}\in\mathbb{B}^{(N+|R_{M}|)\times(N+|R_{M}|)}\). For each event in \(T\) or \(C\), we assume that the start and end token embeddings of its trigger word are \({\bf h}_{i}^{M}\) and \({\bf h}_{j}^{M}\), respectively. Further, for each role involved in the event, we assume that the token embedding is \({\bf r}_{k}^{M}\). Then, we conduct linking operations to bridge the trigger and the involved role. In details, we construct the linking pairs \((i,k+N)\) and \((k+N,j)\), and set \(L_{M}[i][k+N]\) and \(L_{M}[k+N][j]\) as _True_. For those token pairs that are not linked, we mark the corresponding values in \({\bf L}_{M}\) as _False_.

Score MatrixFollowing Tang et al. (2022), we inherit the multi-head self-attention results of the transformer-based PLM as the linking scores between token pairs. Specifically, we average the multi-head self-attention weights without Softmax-normalization from the last layer of the PLM:

\[{\bf A}_{M}=\frac{1}{P}\sum_{p}^{P}\frac{{\bf Q}_{p}{\bf K}_{p}^{\top}}{\sqrt {d_{h}}} \tag{2}\]

where \(P\) is the number of heads, \(d_{h}\) is the dimension of queries and keys, and \({\bf Q}_{p}\) and \({\bf K}_{p}\) are the query and key matrices, respectively. \({\bf A}_{M}\in\mathbb{R}^{(N+|R_{M}|)\times(N+|R_{M}|)}\) represents the trigger-role linking scores for the multiple events.

Training LossWe use the loss \(\mathcal{L}_{\text{TR}}\) to learn the auxiliary task, which guides the connections between different event triggers and their corresponding involved roles as follows:

\[\begin{split}\mathcal{L}_{\text{TR}}=&-\frac{1}{(N+ |R_{M}|)^{2}}\sum_{i}\sum_{j}\left(L_{i,j}^{M}\log\sigma\left(A_{M}[i][j]\right) +\right.\\ &\left.(1-L_{i,j}^{M})\log\left(1-\sigma\left(A_{M}[i][j]\right) \right)\right)\end{split} \tag{3}\]

where \(\sigma\) denotes a sigmoid function. \(L_{i,j}^{M}\) is set to 1 when \(L_{M}[i][j]\) is _True_, while \(L_{i,j}^{M}\) is set to 0 if otherwise.

### Linking Construction for Target Event

To extract arguments for the given target event \((e,t,\mathcal{R}^{c})\), we jointly encode text and event-specific roles, define the label matrix and score matrix for multiple token linkings and present the training loss in this module.

EncodingWe verbalize and concatenate all argument roles in the role set \(\mathcal{R}^{e}\) as a token sequence \(R_{S}\). Then, we follow Ma et al. (2022) to insert two special tokens \(\langle\ell\rangle\) and \(\langle r\rangle\) into the text \(X\) to mark the position of the trigger \(t\):

\[X_{S}=(x_{1},\cdots,\langle\ell\rangle,t,\langle r\rangle,\cdots,x_{|X|}) \tag{4}\]

After that, we concatenate the trigger-aware text \(X_{S}\) and the sequence \(R_{S}\) and leverage another PLM to encode them:

\[{\bf E}^{S}=({\bf h}_{1}^{S},\cdots,{\bf h}_{N+2}^{S},{\bf r}_{1}^{S},\cdots,{ \bf r}_{|R_{S}|}^{S})=\textsc{PLM}(X_{S}\oplus R_{S}) \tag{5}\]

where \({\bf h}_{n}^{S}~{}(1\leq n\leq N+2)~{}~{}\mbox{and}~{}~{}{\bf r}_{n}^{S}~{}(1 \leq n\leq|R_{S}|)\) are the token embeddings within \(X_{S}\) and \(R_{S}\), respectively.

Label MatrixWe define a label matrix to tag argument spans and roles in the given target event, denoted as \({\bf L}_{S}\in\mathbb{B}^{(N+|R_{S}|+2)\times(N+|R_{S}|+2)}\). For each argument, we assume that its role embedding

Figure 2: The overall architecture of our proposed model.

 

is \({\bf r}_{k}^{S}\), and its start and end token embeddings are \({\bf h}_{i}^{S}\) and \({\bf h}_{j}^{S}\), respectively. We first construct the linking pairs \((i,j)\) and \((j,i)\) to tag the argument span, and set \(L_{S}[i][j]\) and \(L_{S}[j][i]\) as _True_. Meanwhile, we tag the role information by argument-role linking operations. In details, we employ two linking pairs \((i,k+N+2)\) and \((k+N+2,j)\), and set \(L_{S}[i][k+N+2]\) and \(L_{S}[k+N+2][j]\) as _True_. Besides, we mark the values in \({\bf L}_{S}\), whose corresponding token pairs are not linked, as _False_.

Score MatrixFollowing Equation (2), we also employ the averaged multi-head self-attention weights from the last layer of the PLM used in the module as the token linking scores, denoted as \({\bf A}_{S}\in\mathbb{R}^{(N+|R_{S}|+2)\times(N+|R_{S}|+2)}\).

Training LossTo leverage the acquired cross-event information, we first employ the two-fold fusion, which will be described in the next module in details, to obtain the final token linking prediction matrix:

\[{\bf P}=\texttt{TFF}({\bf E}^{M},{\bf E}^{S},{\bf A}_{S}) \tag{6}\]

where TFF refers to the two-fold fusion. Then, we obtain the loss \(\mathcal{L}_{\text{EAE}}\) as follows:

\[\begin{split}\mathcal{L}_{\text{EAE}}&=-\frac{1}{( N+|R_{S}|+2)^{2}}\sum_{i}\sum_{j}\left(L_{i,j}^{S}\log P[i][j]+\right.\\ &\quad\left.(1-L_{i,j}^{S})\log\left(1-P[i][j]\right)\right)\end{split} \tag{7}\]

where \(L_{i,j}^{S}\) is set to 1 when \(L_{S}[i][j]\) is _True_, while \(L_{i,j}^{S}\) is set to 0 if otherwise.

### Two-fold Fusion

In this module, we utilize the learned text representations with aggregated cross-event role information to enhance EAE for the target event. Specifically, we first dynamically fuse the text representations from the above two linking modules, referred to as the first-fold fusion. Then, we use the fused text representations to obtain another token linking score matrix for the target event, different from \({\bf A}_{S}\). These two token linking score matrices are further fused as the final linking predictions, referred to as the second-fold fusion.

First-fold FusionAs the encoded text embeddings in \({\bf E}^{M}\) are involved with multiple events, we first leverage conditional layer normalization (CLN) Su et al. (2021); Yu et al. (2021); Xu et al. (2022a) to generate the contextual embeddings for the target event (trigger). For the target trigger \(t\), we simply feed it into a frozen PLM and employ the first token embedding to represent it, denoted as \({\bf t}\). For each token embedding \({\bf h}_{n}^{M}\left(1\leq n\leq N\right)\) in \({\bf E}^{M}\), the integrated embedding \(\tilde{{\bf h}}_{n}^{M}\left(1\leq n\leq N\right)\) is acquired as follows:

\[\alpha_{t}={\bf tW}_{\alpha}+{\bf b}_{\alpha} \tag{8}\]

\[\beta_{t}={\bf tW}_{\beta}+{\bf b}_{\beta} \tag{9}\]

\[\hat{{\bf h}}_{n}^{M}=\text{CLN}({\bf h}_{n}^{M},\alpha_{t},\beta_{t})=\alpha _{t}\odot\left(\frac{{\bf h}_{n}^{M}-\mu}{\sigma}\right)+\beta_{t} \tag{10}\]

where \({\bf W}_{\alpha}\in\mathbb{R}^{d_{1}\times d_{1}}\), \({\bf W}_{\beta}\in\mathbb{R}^{d_{1}\times d_{1}}\), \({\bf b}_{\alpha}\in\mathbb{R}^{d_{1}}\) and \({\bf b}_{\beta}\in\mathbb{R}^{d_{1}}\) are trainable parameters, \(\mu\) and \(\sigma\) are the mean and standard deviation calculated across the elements of \({\bf h}_{n}^{M}\), respectively. Note that \(d_{1}\) is the dimension of \({\bf t}\). Then, \((\hat{{\bf h}}_{1}^{M},\cdots,\hat{{\bf h}}_{N}^{M})\) are inserted with the zero vector **0** to facilitate the alignment with the text embeddings in \({\bf E}^{S}\):

\[({\bf B}_{1}^{M},\cdots,{\bf R}_{N+2}^{M})=({\bf\bar{h}}_{1}^{M},\cdots,{\bf 0 },{\bf\bar{h}}_{1}^{M},\cdots,{\bf\bar{h}}_{J}^{M},{\bf 0},\cdots,{\bf\bar{h}}_{N}^{ M}) \tag{11}\]

where \(i\) and \(j\) correspond to the start and end positions for the token embeddings of the trigger \(t\) in \(({\bf h}_{1}^{S},\cdots,{\bf h}_{N+2}^{S})\). Finally, we dynamically fuse the different text embeddings. Given two token embeddings \({\bf h}_{n}^{S}\) and \(\widetilde{{\bf h}}_{n}^{M}\)\((1\leq n\leq N+2)\), we use a fusion gate mechanism to obtain the token embedding \({\bf h}_{n}^{F}\) as follows:

\[{\bf g}_{n}=\sigma\left(\left[{\bf h}_{s}^{S};\widehat{{\bf h}}_{n}^{M}\right]{ \bf W}_{G}\right) \tag{12}\]

\[{\bf h}_{n}^{F}={\bf g}_{n}\odot{\bf h}_{n}^{S}+(1-{\bf g}_{n})\odot{\bf\bar{ h}}_{n}^{M} \tag{13}\]

where \(\odot\) denotes the element-wise multiplication operation and \({\bf W}_{G}\in\mathbb{R}^{2d_{1}\times d_{1}}\) is a trainable matrix. Note that \(d_{1}\) is the dimension of \({\bf h}_{n}^{S}\) and \(\overline{{\bf h}}_{n}^{M}\).

Second-fold FusionWe concatenate the fused text embeddings \(({\bf h}_{1}^{F},\cdots,{\bf h}_{N+2}^{F})\) and the role embeddings \(({\bf r}_{1}^{S},\cdots,{\bf r}_{|R_{S}|}^{S})\), denoted as \({\bf F}\). Then, we calculate another token linking score matrix \({\bf A}_{F}\in\mathbb{R}^{(N+|R_{S}|+2)\times(N+|R_{S}|+2)}\) as follows:

\[{\bf F}_{Q}={\bf FW}_{Q},\,{\bf F}_{K}={\bf FW}_{K},\,{\bf A}_{F}={\bf F}_{Q}{ \bf F}_{K}^{\top} \tag{14}\]

where \({\bf W}_{Q}\in\mathbb{R}^{d_{1}\times d_{2}}\) and \({\bf W}_{K}\in\mathbb{R}^{d_{1}\times d_{2}}\) are trainable matrices. After that, we fuse the two token linking score matrices \({\bf A}_{S}\) and \({\bf A}_{F}\) using a blending layer Wolpert (1992):

\[{\bf P}=\sigma({\bf A}_{S}+{\bf A}_{F}-\tau) \tag{15}\]

where \(\tau\) is a trainable parameter 



### Training and Inference

TrainingWe introduce the overall training loss as follows:

\[{\cal L}=\alpha{\cal L}_{\text{EAE}}+(1-\alpha){\cal L}_{\text{TR}} \tag{16}\]

where \(\alpha\left(0<\alpha<1\right)\) represents a weight hyperparameter.

InferenceBased on the token linking prediction matrix \(\mathbf{P}\), we first add each linking pair \((i,j)\) to the predicted linking pair set \(\mathcal{C}\) when its prediction value \(P[i][j](1\leq i,j\leq N+|R_{S}|+2)\) exceeds a threshold hyperparameter, denoted as \(\delta\). Then, we employ \(\mathcal{C}\) as input to run the inference algorithm as shown in Algorithm 1 and obtain the extracted argument set \(\mathcal{A}\) of the target event. For each item \((start,end,role)\) in \(\mathcal{A}\), \(start\) and \(end\) denote the start and end positions of an argument, respectively, and \(role\) is the index of the argument role in \(R_{S}\).

```
0: Predicted set \(\mathcal{C}(s_{k},e_{k})),k\in(1,\cdots,|\mathcal{C}|)\).
0: Argument set \(\mathcal{A}\).
1: Let \(cand\_span=set()\).
2: Let \(starr2\_dict=\{\}\), \(end2r\_dict=\{\}\).
3:for\((s_{k},e_{k})\in\mathcal{C}\)do
4:if\(s_{k}\times N+2\textbf{ and }e_{k}\leq N+2\textbf{ then}\)
5:\(camd\_span.add((s_{k},e_{k}))\)
6:else if\(s_{k}\geq N+2\textbf{ and }e_{k}>N+2\textbf{ then}\)
7:\(scatter2\_dict[s_{k}].append(e_{k}-N-2)\)
8:else if\(s_{k}>N+2\textbf{ and }e_{k}>N+2\textbf{ then}\)
9:\(end2r\_dict[e_{k}].append(s_{k}-N-2)\)
10:endif
11:endfor
12:for\((s,e)\in cand\_span\)do
13:for\(r\in(\text{est}(start2r\_dict[s])\cap\text{set}(end2r\_dict[e]))\) do
14:\(\mathcal{A}\)add\(((s,e,r))\)
15:endfor
16:for\(r\in(\text{set}(start2r\_dict[e])\cap\text{set}(end2r\_dict[s]))\)do
17:\(\mathcal{A}\)add\(((e,s,r))\)
18:endfor
19:endfor
```

**Algorithm 1** Inference Process

## 4 Performance Evaluation

### Experimental Setup

DatasetsTo evaluate our proposed model, we conduct experiments on one sentence-level dataset ACE05 Doddington et al. (2004) and three document-level datasets, including RAMS Ebner et al. (2020), WikiEvents Li et al. (2021) and MLEE Pyysalo et al. (2012). Following Ma et al. (2022), we preprocess ACE05 by using the scripts of DyGIIE++ Wadden et al. (2019). As for the document-level datasets, we follow He et al. (2023) to employ a predefined window length, which is set to 250, to split each document into context segments. See the dataset details in Appendix A.

MetricsWe follow previous works Ma et al. (2022); He et al. (2023) to adopt two metrics to measure the performance. (1) Argument Identification F1 (Arg-I): Regard an argument of an event identified correctly when its boundary agrees with any golden arguments of the event. (2) Argument Classification F1 (Arg-C): Regard an argument of an event classified correctly when its boundary and role agree with any golden arguments of the event.

Implementation DetailsFor a fair comparison with recent works, we leverage RoBERTa-base and RoBERTa-large Liu et al. (2019) as the PLM in our model. Specifically, we train the base model on one NVIDIA RTX 3090 24G GPU and the large model on one NVIDIA A100 40G GPU. The Adam optimizer with a linear learning rate scheduler and the warmup strategy with a ratio of 0.1 are adopted. As \(\delta\) serves as the inference threshold of the binary classification prediction value \(P[i][j]\) in Equation (7), we follow the most binary classification setting to set \(\delta\) as 0.5 for all the datasets. For the other hyperparameters, we attach the details in Appendix B.

BaselinesWe compare our Sep2F with the following models, all of which evaluate the EAE performance on **both sentence-level and document-level datasets**: EEQA Du and Cardiie (2020), BART-Gen Li et al. (2021), PAIE Ma et al. (2022), EDGE Li et al. (2023b), APESingle Zhang et al. (2023), TabEAE He et al. (2023). Moreover, ChatGPT equipped with in-context learning (ICL) Brown et al. (2020) has presented impressive performance in various NLP tasks. Still, there needs to be a comprehensive evaluation of different EAE datasets, especially document-level datasets. Thus, we follow Han et al. (2023) to construct 5-shot ICL prompts as input for each test sample and use the OpenAI API access1 to acquire the EAE results. Specifically, we evaluate the EAE performance with two versions of ChatGPT: _gpt-3.5-turbo_ and _gpt-4_. See Appendix D for detailed prompt construction.

Footnote 1: [https://platform.openai.com/](https://platform.openai.com/)

for a target dataset and benefit from the extra resources. Thus, we exclude them from our main results but leave the comparison in Appendix E.

### Results and Analysis

#### Main Results

As shown in Table 1, we first summarize that our large model achieves new state-of-the-art (SOTA) performance on all the datasets. Specifically, compared with the latest SOTA model TabEAE, our large model obtains **1.6%/2.0%**, **1.4%/1.0%**, **2.6%/2.8%** and **2.4%/2.5%** absolute improvements in Arg-I/Arg-C F1 on ACE05, RAMS, WikiEvents and MLEE, respectively. Moreover, our base model averagely exceeds EDGE, which only leverages base PLMs, by 2.5%/3.6% in Arg-I/Arg-C F1 on the three used datasets. Also, using the smaller PLM, our base model outperforms all the baselines powered by large PLMs on WikiEvents and MLEE and obtains competitive performance on ACE05 and RAMS. The results indicate our proposed Sep2F exhibits outstanding performance in handling EAE of different levels.

In addition, we find that both versions of ChatGPT significantly fall behind existing supervised EAE models. The performance gap is usually extended when dealing with the more challenging document-level EAE task. Therefore, how to push LLMs such as ChatGPT to achieve comparable performance for EAE remains to be explored.

Comparison with Different Token Linking ModelsAs mentioned in Introduction, there are mainly two existing categories of methods performing multiple token linking operations for EAE: _single-event extraction_ and _multiple-event extraction_. However, these methods solve EAE as a subpart of end-to-end universal information extraction or event extraction tasks. Thus, their experimental settings and training datasets differ from handling EAE alone. For a fair comparison, we follow the details of these token linking methods to design the following variants: (1) **SingleE** trains the Linking Construction for Target Event module without the two-fold fusion to extract arguments of each single event. It corresponds to _single-event extraction_. (2) **MultiE** extracts arguments of multiple events simultaneously, which corresponds to _multilevent extraction_. Specifically, we formulate such EAE as a multiple <trigger-role-argument> triple extraction2 and refer to the implementation3 of UniRel Tang et al. (2022), which conducts their relational triple extraction with multi-token entities. Note that the roles concatenated with input text are only specific to the involved events. (3) **MultiE-L** concatenates all pre-defined roles of the

\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline Model & PLM & \multicolumn{2}{c}{ACE05} & \multicolumn{2}{c}{RAMS} & \multicolumn{2}{c}{WikiEvents} & \multicolumn{2}{c}{MLEE} \\ \cline{3-9}  & & Arg-I & Arg-C & Arg-I & Arg-C & Arg-I & Arg-C & Arg-I & Arg-C \\ \hline ChatGPT(S-shot ICL) & GPT-3.5 & 35.6 & 30.0 & 25.1 & 19.3 & 13.6 & 11.6 & 15.7 & 11.8 \\ ChatGPT(5-shot ICL) & GPT-4 & 37.5 & 33.2 & 23.8 & 20.9 & 16.7 & 15.3 & 16.9 & 14.9 \\ \hline EEGA Du and Cardie (2020)* & RoBERTa-l & 72.1 & 70.4 & 51.9 & 47.5 & 60.4 & 57.2 & 70.3 & 68.7 \\ BART-Gen Li et al. (2021) & BART-l & 69.9 & 66.7 & 51.2 & 47.1 & 66.8 & 62.4 & 71.0 & 69.8 \\ PAIE Ma et al. (2022) & BART-l & 75.7 & 72.7 & 56.8 & 52.2 & 70.5 & 65.3 & 72.1 & 70.8 \\ PAIE Ma et al. (2022)* & RoBERTa-l & 76.1 & 73.0 & 57.1 & 52.3 & 70.9 & 65.5 & 72.5 & 71.4 \\ EDGE Li et al. (2023b) & BART-b & 75.3 & 70.6 & 55.2 & 49.7 & 68.2 & 62.8 & - & - \\ APE(Single) Zhang et al. (2023) & BART-l & 75.3 & 72.9 & 56.3 & 51.7 & 70.6 & 65.8 & - & - \\ TabEAE He et al. (2023) & RoBERTa-l & 77.2 & 75.0 & 57.3 & 52.7 & 71.4 & 66.5 & 75.1 & 74.2 \\ \hline Sep2F Ours & RoBERTa-b & 76.2 & 73.5 & 56.6 & 52.1 & 73.3 & 68.2 & 76.8 & 75.6 \\ Sep2F Ours & RoBERTa-l & **78.8** & **77.0** & **58.7** & **53.7** & **74.0** & **69.3** & **77.5** & **76.7** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results based on four datasets. The best score is in bold and the second best score is underlined. * indicates the results from He et al. (2023). b and l in the column PLM represent the base and large models, respectively. Note that we report the averaged results of our Sep2F with three different fixed random seeds.

\begin{table}
\begin{tabular}{l c c c} \hline \hline Model & ACE05 & RAMS & WikiEvents & MLEE \\  & [1.35] & [1.25] & [1.78] & [3.32] \\ \hline SingleE & 75.4 & 53.0 & 66.5 & 74.4 \\ MultiE & 69.9 & 49.3 & 47.9 & 57.2 \\ MultiE-L & 68.1 & 40.5 & 26.5 & 8.4 \\ Sep2F & **77.0** & **53.7** & **69.3** & **76.7** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison in Arg-C F1 (%) between different multiple token linking models. We present the average number of events per instance for each dataset in \([\cdot]\). The performance is reported based on RoBERTa-large.

 

dataset with input text. As for the other settings, it follows MultiE.

From the results in Table 2, we conclude that our model surpasses all variants on the four datasets. Compared with our Sep2F, the performance of SingleE, which does not leverage cross-event information, drops by 1.6%, 0.7%, 2.8% and 2.3% in ArgC F1 on ACE05, RAMS, WikiEvents and MLEE, respectively. Further, we see that MultiE fails to acquire competitive performance for EAE. Especially on WikiEvents and MLEE, the performance gap between MultiE and our model is rather significant. The main reason is that the average number of events per instance on these two datasets is larger than the others. As a result, MultiE struggles to handle more token linking predictions for each instance. Besides, we observe that MultiE-L generally lags behind MultiE. As expected, the more concatenated roles make the correct role choices more challenging when performing linking predictions between text and roles. Hence, a longer role concatenation will impair the performance of multiple token linking models.

Detailed Results on Single/Multiple EventsFollowing He et al. (2023), we divide the test instances of each dataset into two groups according to the number of events (i.e., \(|C|\)) surrounding the target extracted event. When \(|C|=0\), only the target extracted event is in the instance. On the other hand, if \(|C|>0\), there are multiple events. Then, we investigate the detailed results based on the groupings. As illustrated in Table 3, we can observe that: (1) On both groups, the Arg-C F1 of our large model exceeds the previous SOTA model TabEAE. We attribute this improvement to our separation-and-fusion paradigm, which preserves both advantages of _single-event extraction_ and _multiple-event extraction_. (2) Our base model demonstrates impressive performance results for the instances containing multiple events on WikiEvents and MLEE, even outperforming the two most competitive models utilizing large-version PLMs, SingleE and TabEAE. It verifies our model is good at handling instances with multiple events. (3) SingleE exhibits a comprehensive performance improvement compared with PAIE, despite both disregarding cross-event information. These results validate the capability of handling EAE using token linking models.

Ablation Study of Two-fold FusionTo analyze the benefit of our proposed two-fold fusion, we conduct an ablation study based on RoBERTa-large. Table 4 illustrates that removing the First or Second-fold Fusion leads to a performance drop.

Figure 3: A case study from WikiEvents. The triggers of different events are in different colors. Note that the involved models are based on RoBERTa-large.

 

This suggests that both fusions contribute quite significantly to our model.

Case StudyHere, we conduct qualitative analysis with a specific instance from WikiEvents. Figure 3 shows the EAE results from our Sep2F and SingleE. We can see that SingleE misses the two arguments in the "_Attack_" event triggered by "_booming_", but our Sep2F gives the correct predictions. We infer that our model can leverage the role information from the event triggered by "_killed_". Similarly, as the cross-event role "_Destroyer_" provides a vital clue, our model avoids the disturbance of the trigger "_death_" and recognizes the role of "_He_" as "_Killer_" rather than "_Victim_", but SingleE fails.

## 5 Conclusion

In this paper, we propose Sep2F, a novel multiple token linking model for EAE. Specifically, we employ two linking modules to separate the acquisition of cross-event information and the argument extraction of a target event. In addition, we propose a novel two-fold fusion module to guarantee that the acquired cross-event information enhances the argument extraction effectively. Therefore, the proposed model can leverage cross-event clues and retain the merits of single-event extraction. Extensive experiments on four widely used benchmarks show our model achieves new state-of-the-art performance.

### Limitations

The limitations of our work are summarized as follows:

* We mainly focus on the EAE task in this paper. As multiple token linking models adapt to different information extraction tasks, such as event detection and relation extraction, we will extend our work and consider different designs for cross-event/cross-relation information acquisition in these tasks.
* How to leverage external resources in our proposed Sep2F remains an open question. The external resources can be other EAE datasets or commonsense knowledge and help enhance the EAE performance.

## Ethics Considerations

Our work adheres to the guidelines outlined in the ACL Code of Ethics. As event argument extraction is a widely accepted and long-standing research task in NLP, we do not see any significant ethical concerns. As for the scientific artifacts used in our experiments, we confirm to comply with the corresponding intended use and licenses.

## Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2022YFC3302100) and the China Postdoctoral Science Foundation (Grant No. 2022TQ0033). This work was also supported by the financial support (No. 202206030113) by China Scholarship Council during a visit to Nanyang Technological University.

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## Appendix A Dataset Details

**ACE05**Doddington et al. (2004) is a sentence-level information extraction dataset including entities, relations and events with three language 

versions. It consists of annotated newspapers, newswire data and broadcast news through the efforts of the Automatic Content Extraction (ACE) program. We use its English event annotation as our evaluation for the sentence-level EAE. As for the data preprocessing, we utilize the scripts of EEQA Du and Cardie (2020), which follows the settings of DyGIE++ Wadden et al. (2019).

**Rams**Ebner et al. (2020) is a document-level EAE dataset derived from news articles. Unlike the original annotations, which treat multiple events in the same context as different instances, we follow the preprocessing procedure of TabEAE He et al. (2023) to aggregate the annotations of multiple events appearing within the same context for each instance. The aggregation setting does not bring additional resources or knowledge. Furthermore, we continue to extract one target event from each instance individually, and the number of instances in RAMS remains unchanged.

**WikiEvents**Li et al. (2021) is a document-level EAE dataset sourced from English Wikipedia. We follow the preprocessing procedure of PAIIz Ma et al. (2022) to employ a pre-defined window centering on each trigger word to avoid exceeding the length constraint of PLMs. It differs from the window setting of TabEAE and facilitates the better utilization of multiple events when keeping the same length of the window.

**Mlee**Pyysalo et al. (2012) is a document-level event extraction dataset annotated from the abstracts of English publications in the biomedical field. We follow the preprocessing procedure of TabEAE, which refers to the work Trieu et al. (2020). Besides, as no development set in MLEE, we follow TabEAE to use the training set to tune our hyperparameters.

StatisticsTable 6 lists the detailed statistics of the above four datasets.

## Appendix B Implementation Details

Following TabEAE, we set the pre-defined window length as 250 on all four datasets. For the training epoch, as our source code refers to the implementation of UniRel Tang et al. (2022), we keep its training epoch settings except for RAMS. A smaller training epoch is chosen because RAMS contains more training instances than the other three datasets. In particular, we search the training epoch within the interval [30, 50] for RAMS. For the batch size, we set a maximum value (a power of 2) that a single GPU can run on the three document-level datasets. For the sentence-level ACE05, we search the batch size within the interval [8, 16]. Note that we first tune the learning rate and the training weight \(\alpha\) by a grid search based on the development set of each dataset. After that, we keep these two hyperparameter settings and tune the other hyperparameters. The tuned intervals and chosen hyperparameters are presented in Table 5.

\begin{table}
\begin{tabular}{c|r r r r} \hline \hline
**Dataset** & **ACE05** & **RAMS** & **WikiEvents** & **MLEE** \\ \hline \#Events & & & & \\ Train & 4,202 & 7,329 & 3,241 & 4,442 \\ Dev & 450 & 924 & 345 & 2,200 \\ Test & 403 & 871 & 365 & 2,200 \\ \hline \#Arps & & & & \\ Train & 4,859 & 17,026 & 4,522 & 5,786 \\ Dev & 605 & 2,188 & 428 & \\ Test & 576 & 2,023 & 566 & 2,764 \\ \hline \#Event Types & 33 & 139 & 50 & 23 \\ \#Rede Types & 22 & 65 & 59 & 8 \\ \#Avg Args & 1.19 & 2.33 & 1.40 & 1.29 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detailed statistics of datasets. _Avg Args_ denotes the average number of arguments per event.

\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
* & 16 & 8 & 8 & 8 \\ Training Epoch &
- & 100 & 30 & 100 & 100 \\ Learning Rate & [3e-5, 4e-5, 5e-5] & 3e-5 & 5e-5 & 4e-5 & 4e-5 \\ Training Weight \(\alpha\) & [0.5, 0.6, 0.7, 0.8, 0.9] & 0.7 & 0.7 & 0.8 & 0.6 \\ Projection Dimension \(d_{2}\) & [32, 64] & 32 & 64 & 64 & 64 \\ \hline Batch Size &
- & 16 & 8 & 8 & 8 \\ Training Epoch &
- & 100 & 50 & 100 & 100 \\ Learning Rate & [1e-5, 2e-5, 3e-5] & 2e-5 & 3e-5 & 2e-5 & 1e-5 \\ Training Weight \(\alpha\) & [0.5, 0.6, 0.7, 0.8, 0.9] & 0.8 & 0.5 & 0.7 & 0.8 \\ Projection Dimension \(d_{2}\) & [32, 64] & 32 & 64 & 64 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter settings. The upper table shows the setting details of our base model, while the bottom table corresponds to our large model.
 



## Appendix C Analysis on Weight Hyperparameters

For the weight hyperparameter \(\alpha\), we maintain the chosen learning rate of each dataset and analyze the performance in Arg-C F1 (%) when tuning it within the interval [0.5, 0.6, 0.7, 0.8, 0.9]. As shown in Table 7, we observe that our Sep2F exhibits relatively stable performance across different values of \(\alpha\). This proves the robustness of Sep2F well. Additionally, when we set \(\alpha\) to 1, it implies that we are unable to utilize the cross-event role labels by tuning the trigger-role loss \(\mathcal{L}_{\text{TR}}\) in Equation (3). As a result, the absence of cross-event role information degrades the performance of our model in Arg-C F1 by 1.2%, 1.8%, 2.3% and 1.9% on ACE05, RAMS, WikiEvents and MLEE, respectively.

## Appendix D Prompt Construction for ChatGPT

To evaluate the EAE performance of ChatGPT, we follow Han et al. (2023) to construct 5-shot in-context learning (ICL) Brown et al. (2020) prompts. Each constructed prompt consists of three components: Instruction, Demonstration and Target. The details of them are listed as follows:

Instructiondescribes EAE and specifies the output format. We use the task instruction and output format description provided by Han et al. (2023).

Demonstrationcontains five randomly sampled training instances. Each sampled training instance includes input text, event trigger information, event type information, event-specific candidate roles and golden argument extraction results. Note that we also provide a short context centering on the trigger in the trigger information. It helps ChatGPT locate the trigger from the possible repeated words.

Targetrefers to the test instance. We provide its input text, event trigger, event type and event-specific candidate roles.

Then, we concatenate and feed the above three parts into ChatGPT to acquire the EAE results for each test instance. Specifically, ChatGPT is expected to output an argument list and each argument consists of its text span and role type. A prompt example is shown in Figure 5.

## Appendix E Comparison with Resource-enhanced Models

We compare our model with UnifiedEAE Zhou et al. (2022) and APE Zhang et al. (2023), which focus on exploring cross-dataset knowledge. In particular, APE pays extra manual efforts to de

Figure 4: Performance comparison with the models trained on multiple datasets. The upper figure illustrates the comparison using base-version PLMs, while the bottom figure shows the results with large-version PLMs.

\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) & ACE05 & RAMS & WikiEvents & MLEE \\ \hline
0.5 & 76.1 & **53.7** & 68.6 & 76.6 \\
0.6 & 76.8 & 53.0 & 68.7 & 76.4 \\
0.7 & 76.7 & 53.1 & **69.3** & 76.6 \\
0.8 & **77.0** & 52.7 & **69.3** & **76.7** \\
0.9 & 76.4 & 52.8 & 67.9 & 76.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Arg-C F1 (%) results with different training weight hyperparameters. The performance is reported based on RoBERTa-large.

Figure 5: A prompt example from WikiEvents.



sign overlap knowledge prompts. From the results in Figure 4, we observe that our Sep2F outperforms UnifiedEAE and APE on all three datasets when using base-version PLMs. Furthermore, our Sep2F performs better than APE on ACE05 and WikiEvents and obtains fairly competitive performance on RAMS when using large-version PLMs. Thus, we summarize that our model achieves the best performance on almost all datasets utilizing different versions of PLMs, though the compared models benefit from additional training resources and manual efforts. The promising results further prove the superiority of our model.