In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tuning for extractive objectives to take the best advantages of Pre-trained Language Models (PLMs). It introduces two span selectors based on the prompt to select start/end tokens among input texts for each role. On the other hand, it captures argument interactions via multi-role prompts and conducts joint optimization with optimal span assignments via a bipartite matching loss. Also, with a flexible prompt design, PAIE can extract multiple arguments with the same role instead of conventional heuristic threshold tuning. We have conducted extensive experiments on three benchmarks, including both sentence-and document-level EAE. The results present promising improvements from PAIE ( 3 . 5% and 2 . 3% F1 gains in average on three benchmarks, for PAIE-base and PAIE-large respectively). Further analysis demonstrates the efficiency, generalization to few-shot settings, and effectiveness of different extractive prompt tuning strategies. Our code is available at https: //github.com/mayubo2333/PAIE . Understanding text by identifying the event and arguments has been a long-standing goal in Natural Language Processing (NLP) (Sundheim, 1992). As shown in Fig. 1, we can quickly understand that the document is talking about a Sell event, with four involved arguments, i.e., Vivendi (Seller), Universal Studios (Artifact), parks (Artifact), and company (Artifact), where the argument roles are in brackets. Since event detection has achieved great success in * Equal Contribution. Dan Sanchez reports : The Saudis Go Full ISIS In Their US Backed Takfiri War on the Shia Saudi Arabia has perpetrated a mass <t> execution </t> that puts ISIS's beach beheadings to shame. Forty-seven heads rolled on Saturday. One of them belonged to Nimr al Nimr, a revered Shi'ite cleric who had been sentenced to death for sermons in which he criticized the government (especially for its persecution of the country 's Shi'ite minority). Cash strapped Vivendi wants to <t> sell </t> Universal Studios, its Universal theme parks and television production company. Defendant Executor Crime Seller Event type: justice.judicialconsequences.execute Event type: Transaction.Transfer-Ownership Artifact Artifact Artifact Sentence-level EAE Document-level EAE Figure 1: Examples of (top) sentence-level and (bot-tom) document-level event argument extraction. recent years (Wang et al., 2021), the main challenge lies in Event Argument Extraction (EAE). Typical efforts in EAE can be roughly classified into two groups. The first group of methods formulates it as a semantic role labeling problem (Wei et al., 2021). There are generally two steps  first identifying candidate spans and then classifying their roles. Although joint models are proposed to optimize them together, high dependence on candidates may still suffer from error propagation (Li et al., 2013). In the second group, recent studies tend to follow the success of Pre-trained Language Models (PLMs) and solve EAE by Question Answering (QA) (Liu et al., 2021a; Wei et al., 2021; Du and Cardie, 2020; Liu et al., 2020; Li et al., 2020) and Text Generation (Lu et al., 2021; Li et al., 2021). QA-based models can effectively recognize the boundaries of arguments with role-specific questions, while the prediction has to be one by one. Generation-based methods are efficient for generating all arguments, but sequential predictions degrade the performance on long-distance and more arguments. Besides, the state-of-the-art performance is still unsatisfactory (around 68% F1 on the widely used dataset ACE05 (Doddington et al., 2004)). Here raises an interesting question, 6759 is there any way to combine the merits of the above methods, as well as to boost the performance? This paper targets real scenarios, which require the EAE model to be effective yet efficient at both sentence and document levels, and even under the few-shot setting without sufficient training data. To do this, we highlight the following questions:  How can we extract all arguments simultaneously for efficiency? How to effectively capture argument interactions for long text, without knowing them in advance? How can we elicit more knowledge from PLMs to lower the needs of annotation? In this paper, we investigate prompt tuning under an extractive setting and propose a novel method PAIE that P rompting A rgument I nteractions for E AE. It extends QA-based models to handle multiple argument extraction and meanwhile takes the best advantage of PLMs. The basic idea is to design suitable templates to prompt all argument roles for PLMs, and obtain role-specific queries to jointly select optimal spans from the text. Thus, instead of unavailable arguments, each role in the template serves as a slot for interactions, and during learning, PLMs tend to fill these slots with exact arguments via a matching loss. By predicting arguments together, PAIE enjoys an efficient and effective learning procedure. Besides, the inter-event knowledge transfer between similar role prompts alleviates the heavy burden of annotation cost. Specifically, for prompting extraction, we design two span selectors based on role prompts, which select start/end tokens among input texts. We explore three types of prompts: manual template, concatenation template, and soft prompt. They perform well at both sentence-level EAE (S-EAE) and document-level EAE (D-EAE) and ease the requirements of the exhaustive prompt design. For joint span selection, we design a bipartite matching loss that makes the least-cost match between predictions and ground truth so that each argument will find the optimal role prompt. It can also deal with multiple arguments with the same role via flexible role prompts instead of heuristic threshold tuning. We summarize our contributions as follow:  We propose a novel model, PAIE, that is effective and efficient for S-EAE and D-EAE, and robust to the few-shot setting. We formulate and investigate prompt tuning under extractive settings, with a joint selection scheme for optimal span assignments. We have conducted extensive experiments on three benchmarks. The results show a promising improvement with PAIE ( 3 . 5% and 2 . 3% F1 gains on average absolutely in base and large model). Further ablation study demonstrates the efficiency and generalization to few-shot settings of our proposed model, as well as the effectiveness of prompt tuning for extraction. Event Argument Extraction: Event Argument Extraction is a challenging sub-task of event extraction (EE). There have been great numbers of studies on EAE tasks since an early stage (Chen et al., 2015; Nguyen et al., 2016; Huang et al., 2018; Yang et al., 2018; Sha et al., 2018; Zheng et al., 2019). Huang and Peng (2021) propose to leverage Deep Value Networks (DVN) that captures cross-event dependencies for EE. Huang and Jia (2021) convert documents to unweighted graph and use GAT to alleviate the role overlapping issue. A common idea is to first identify argument candidates and then fill each with a specific role via multi-label classification (Lin et al., 2020). To deal with implicit arguments and multiple events, Xu et al. (2021) construct a heterogeneous graph of arguments, while DEFNN (Yang et al., 2021) predicts arguments via Parallel Prediction Networks. A recent trend formulates EAE as an extractive question answering (QA) problem (Du and Cardie, 2020; Liu et al., 2020). This paradigm naturally induces the language knowledge from pre-trained language models by converting EAE tasks to fully-explored reading comprehension tasks via a question template. Wei et al. (2021) considers the implicit interaction among roles by adding constraints with each other in template, while Liu et al. (2021a) leverages data augmentation to improve the performance. However, they can only predict roles one by one, which is inefficient and usually leads to sub-optimal performance. With the help of the pre-trained Encoder-Decoder Transformer architecture, such as BART (Lewis et al., 2020) and T5 (Raffel et al., 2020), there are also some recent works converting extraction tasks to generation tasks. Paolini et al. (2021) propose TANL to handle a variety of structured prediction tasks, including EAE, by a unified text-to-text approach and extract all arguments in a single pass. Lu et al. (2021) follow 6760 TANL and also take EAE as a sequential generation problem. Li et al. (2021) target generation model by designing specific templates for each event type. In comparison, we prompt argument interactions to guide PLMs and optimize the multiple argument detection by designing a bipartite matching loss. This not only improves the understanding of long-distance argument dependencies but also enjoys an efficient procedure via prompt-based learning. Prompt-based Learning: Prompt-based learning is a new paradigm emerging in the field of pre-trained language models (Liu et al., 2021b). Unlike the pre-training and fine-tuning paradigm, prompt-based methods convert the downstream tasks to the form more consistent with the model's pre-training tasks. Schick and Schtze (2021) convert a variety of classification problems to cloze tasks by constructing related prompts with blanks and finding a mapping from particular filled words to predicted categories. Li and Liang (2021) focus on generation tasks and propose lightweight prefix tuning by freezing model parameters and only adjusting a sequence of continuous task-specific vectors. Different from the above prompt tuning methods designed for classification or generation tasks, our proposed method returns to linear head setting for fitting extraction task better. It is somewhat similar as a concurrent work P-tuning v2 (Liu et al., 2021c). PAIE considers multiple arguments and their interactions to prompt PLMs for joint extraction. Our model, as illustrated in Fig. 2, contains three core components: prompt creation , span selector decoding , and span prediction . In the following sections, we will first formulate prompt for extraction, and describe each component in turn. Existing prompt-based methods mainly focus on classification and generation tasks. Conventional extraction objectives are converted into a generation task. This brings an inefficiency issue that the model has to enumerate all of extraction candidates. For example, Cui et al. (2021) design the prompt for named entity recognition: [candidate span] is [entity type/not a] entity . The models need to fill the first slot with candidate entities, and check the outputs of LM for the second slot for extraction. Can prompt-based methods directly be applied on extraction? since the basic idea is similar with clas-sification/generalization  comparing the slot em-beddings with label vocabulary/input tokens. Here, we give a formulation about the general extractive prompting method and then apply it on EAE for a case study. (1) Prompt Creation . Given context X and a series of queries Q = { q 1 , q 2 , ..., q K } , we create a joint prompt containing all these queries, where f prompt is the prompt creator. (2) Prompted Selector Decoding . Given a PLM L , context X , and prompt P t , we decode a query-specific (answering) span selector as follows:  q k = h L ( q k ; P t, X ) where q k is the k -th query in the prompt and h L is the outputs of PLMs. (3) Prompted Span Selection . To find the optimal span, we design two selectors for the start and end tokens from context: ( s, e ) q k = Span-search [ g L ( X ;  q )] where ( s, e ) q k is the span about k -th query and g L is the span selector. Clearly, such formulation is better than generative extraction by mainly considering the adjacent constraints of span. Task Definition We formulate EAE task as a prompt-based span extraction problem on dataset D . Given an instance ( X, t, e, R ( e ) )  D , where X denotes the context, t  X denotes the trigger word, e denotes the event type and R ( e ) denotes the set of event-specific role types, we aim to extract a set of span A . Each a ( r )  A is a segmentation of X and represents an argument about r  R ( e ) . We create a set of prompts for each event type e in dataset D . Each prompt contains all roles r  R ( e ) . For example in Fig.2, given event type e as negotiate and R ( e ) as { Participant , Topic , Place } , the prompt P t ( e ) may be defined as follows: Participant communicated with Participant about Topic at Place . We call the mentions of roles in the prompt as slot , and there are four slots underlined in this example (and colored in Fig. 2). Such design allows our model to capture the implicit interactions among different roles. To avoid threshold tuning for multiple arguments with the same role, the prompt is flexible to use multiple slots for the same role, such as role Participant in the above example. The number of slots for the role is heuristically determined according to the maximum number of arguments of each role in the training dataset. We design three different prompt creators f prompt , the mapping from a set of roles to a prompt as follows: 1. Manual Template: All roles are connected manually with natural language. We follow the template from Li et al. (2021) for fair comparison. 2. Soft Prompt: Following Qin and Eisner (2021) and Liu et al. (2021d), we connect different roles with learnable, role-specific pseudo tokens. 3. Concatenation Template: To concatenate all role names belonging to one event type. We give one example of these three types of prompt in Table 1 and list more examples in Appendix B. Further analysis can be found in Section 5.2. Given context X and prompt P t , this module generates the role-specific span selector  k , for each slot k of the prompt. Here we choose L as BART (Lewis et al., 2020), a standard Transformer-based pre-trained language model consisting both an Encoder and a Decoder : L = [ L enc , L dec ] . We first define text markers  t  and  /t  as special tokens then insert them into context X before and after the trigger word respectively. into BART-Encoder and the prompt into BART-Decoder separately, as illustrated in Fig. 2. The prompt and context would interact with each other at the cross-attention layers in the decoder module. H ( enc ) X = L enc (  X ) HX = L dec ( H ( enc ) X ; H ( enc ) X ) H pt = L dec ( P t ; H ( enc ) X ) (1) where HX denotes the event-oriented context representation and H pt denotes context-oriented prompt representation. For k -th slot in the joint prompt we mean-pool its corresponding representations from h pt and obtain role feature  k  R h , where h denotes the dimension of hidden layer in BART. Note that a role may have multiple slots and, correspondingly, multiple role features and span selectors. We adopt a simple but effective modification on previous QA-based methods by deriving role-specific span selector  k from every role feature in the prompt. Given role feature  k , we have:  ( start ) k =  k  w ( start )  R h  ( end ) k =  k  w ( end )  R h (2) where  = [ w ( start ) ; w ( end ) ]  R h  2 is learnable parameters shared among all roles, and  represents element-wise multiplication. k = [  ( start ) k ;  ( end ) k ] is exactly the span selector for k -th slot in the prompt. With only one meta-head  and simple operations, our method enables to generate arbitrary number of role-specific span selectors to extract related arguments from context. Recall the generation process of role feature  k from prompt h pt , it is obvious that both the interaction among different roles and the information 6762 Prompt Type Prompt Example MA Template Victor ( and Victor ) defeated in ConflictOrElection at Place ( and Place ) SF Prompt <Vic_left0> Victor <Vic_right0> ( <Vic_left0> Victor <Vic_right0> ) <Conf_left0> ConflictOrElection <Conf_right0> <Place_left0> Place <Place_right0> ( <Place_left0> Place <Place_right0> ) CA Template Victor ( Victor ) ConflictOrElection Place ( Place ) Table 1: Variants of prompt introduced in section 3.2. Given context representation HX and a set of span selectors {  k } , each  k aims to extract at most one corresponding argument span ( s k , e k ) from HX . For  k relating to one argument a k =  X i : j , where i and j are the start and end word indices in context, the selector is expected to output ( s k ,  e k ) = ( i, j ) as prediction. And for  k relating to no argument (when context has no argument about this role, or the slot number of this role exceeds the argument number), it is expected to output ( s k ,  e k ) = (0 , 0) representing an empty argument  . We first follow the extractive prompt formulation in Section 3.1 to calculate the distribution of each token being selected as the start/end of the argument for each role feature. logit ( start ) k =  ( start ) k HX  RL logit ( end ) k =  ( end ) k HX  RL (3) where logit ( start ) k and logit ( end ) k represent start and end position distributions over the context tokens for each slot k , and L denotes the context length. Then we calculate probabilities where the start/end positions locate: p ( start ) k = Softmax ( logit ( start ) k )  RL p ( end ) k = Softmax ( logit ( end ) k )  RL (4) and define the loss function as: L k ( X ) =  (log p ( start ) k ( s k ) + log p ( end ) k ( e k )) L = (cid:88) X  D (cid:88) k L k ( X ) (5) where D ranges over all context in dataset and k ranges over all slots in prompt for X . Bipartite Matching We optionally introduce bipartite matching to deal with multiple arguments of the same role for finding the global-optimal assignments with the least-cost match. Since we insert multiple slots about this role and each slot generates one prediction, it is a canonical bipartite matching problem that matches local-optimal predictions (of each slot) and ground truth as much as possible. Following Carion et al. (2020); Yang et al. (2021), we use Hungarian algorithm (Kuhn, 1955) and leave the detail about it in Appendix A.4. For inference, we define the set of candidate spans for event arguments as C = { ( i, j ) | ( i, j )  L 2 , 0 < j  i  l }  { (0 , 0) } . It contains all spans shorter than the threshold l and special span (0 , 0) indicating no arguments. Our model extracts the argument of each span selector  k by enumerating and scoring all candidate spans as: score k ( i, j ) = logit ( start ) k ( i ) + logit ( end ) k ( j ) (6) and the predicted span of slot k is given by: (  s k ,  e k ) = arg max ( i,j ) C score k ( i, j ) (7) Since at most one span is predicted by each slot in the prompt, this strategy avoids the exhaustive threshold tuning. In this section, we explore the following questions: Can PAIE better utilize PLMs for joint extraction to boost the performance of S-EAE and D-EAE? How do different prompt training strategies affect the results? How does PAIE perform in various practical settings, including efficiency and generalization to few-shot, long-distance, and multiple arguments? Datasets We conduct experiments on three common datasets in Event Argument Extraction task: RAMS (Ebner et al., 2020), WIKIEVENTS (Li et al., 2021) and ACE05 (Doddington et al., 2004). RAMS and WIKIEVENTS are latest document-level EAE benchmarks, while ACE05 is a classical dataset commonly used for sentence-level EAE task. We leave the dataset details in Appendix A.1. Evaluation Metric We adopt two evaluation metrics. (1) Argument Identification F1 score (Arg-I): an event argument is correctly identified if its offsets and event type match those of any of the argument mentions. (2) Argument Classification F1 score (Arg-C): an event argument is correctly classified if its role type is also correct. For WIKIEVENTS dataset, we follow (Li et al., 2021) and additionally evaluate Argument Head F1 score (Head-C), which only concerns the matching of the headword of an argument. Baselines We compare PAIE with several state-of-the-art models in three categories: (1) Multi-label classification model: ONEIE (Lin et al., 2020) (2) Generation model: BART-Gen (Li et al., 2021) (3) QA-based model: EEQA (Du and Cardie, 2020), DocMRC (Liu et al., 2021a) and FEAE (Wei et al., 2021). For a fair comparison, we replace the PLMs used in the strongest baseline EEQA with BART, the same with PAIE, namely EEQA-BART . More details of baselines are listed in Appendix A.2. Table 2 compares our approach with all baselines. We observe that PAIE performs best on all datasets. For S-EAE, our base model achieves an absolute Arg-C improvement of 2 . 1% on ACE05. For D-EAE, our base model obtains 2 . 1% and 6 . 3% Arg-C gains on RAMS and WIKIEVENTS, respectively. Similarly, our large-version model achieves 3 . 5% and 2 . 9% gains. This demonstrates a good generalization ability of our proposed method on dealing with varying lengths of context. We also find that QA-based model sometimes performs well even in document-level EAE tasks. The EEQA-BART model shows almost the same Arg-C with BART-Gen (Li et al., 2021) on RAMS dataset. Other QA-based models (especially those considering interactions among arguments, like FEAE (Wei et al., 2021)) also have competitive performance. As for WIKIEVENTS, however, QA-based models are inferior to sequential-generation models significantly. We speculate that the performance of previous QA-based models is not robust to handle longer text. Both BART-Gen (Li et al., 2021) and our model PAIE have a relatively stable performance on various document-level EAE datasets, but our model performs better, especially with smaller PLMs. In this section, we investigate the effectiveness of our main components by removing each module in turn. (1) bipartite matching . We drop out of the bipartite matching loss and ignore the global opti-6764 Model Bipartite Matching Multi-arg Prompt Role-specific Selector PLM Arg-C ACE05 RAMS WIKI PAIE    BART-b 69 . mal span assignment. (2) multi-arg prompt . We additionally replace the prompt containing multiple roles with several single templates in which include only one role. (3) role-specific selector . The selector is not role-specific anymore but is shared among all roles. This variant degrades to EEQA-BART. We summarize the results of ablation studies in Table 3. (1) EEQA-BART outperforms EEQA significantly, which demonstrates that even conventional QA-based methods have substantial space for improvement with a better PLM and span selection strategy. (2) The role-specific selector further improves Arg-C scores in RAMS and WIKIEVENTS, while taking a slightly negative effect on ACE05. Since the former two datasets are document-level and have more role types (65 in RAMS, 59 in WIKIEVENTS, and 36 in ACE05), we speculate that role-specific selector plays a critical role when identifying and disambiguating roles with complicated ontology structures in long documents. (3) Joint multi-argument prompt achieves consistent improvement on all three datasets. It indicates that the joint prompt has the potential to capture implicit interaction among arguments. (4) Bipartite matching loss has an average improvement of 0 . 7% on three benchmarks. We conjectured it is due to the permutation-invariance property of bipartite matching and discuss further in Appendix A.5. PAIE feeds the context into BART-Encoder and the prompt into BART-Decoder respectively. A plausible and straightforward variant called PAIEE (PAIE-Encoder) concatenates context and prompt, then feed them into encoder directly. We investigate the performance of PAIEE compared with PAIE in this section, as shown in Table 4. We can see that concatenating context and prompt slightly impairs the model performance. It seemingly indicates that the over-interaction be-Variant PLM ACE05 RAMS WIKI PAIEE BE-b 65.9 46.3 62.9 BA-b 70.2 49.3 62.8 BA-l 72.3 51.7 65.1 PAIE BA-b 69.8 49.5 63.4 BA-l 72.7 52.2 65.3 Table 4: Arg-C F1 of different PLMs. tween context and prompt is not of benefit. Furthermore, the prompt squeezes the limited input length of the encoder kept for a document if it concatenates with the document. The experiments support our strategy feeding context and prompt separately without concatenation to PAIE. We investigate how different types of prompts affect the performance in this section, as shown in Fig. 3. We compare four different prompts: three joint prompts introduced in Section 3.2 and one single template containing only one role slot, i.e. the question template used in QA-based method. We find that (1) All three joint prompts outperform the single template, which validates the effectiveness of the joint prompt. (2) Manual template has the most stable performance and usually the better result than others. (3) Soft prompt achieves comparable result with a manual template. We claim this observation inspiring because the creation of the manual template is laborious and soft prompts almost avoid such a handcrafted process. It also accords with current trends of creating distinct continuous prompts, which usually perform better than manual ones. (4) Concatenation template performs worst among joint prompts. We conjecture it is due to such prompt neither contains prior knowledge about role interaction (manual template) nor learns such interaction during training (soft prompt). In D-EAE task, arguments could span multiple sentences. Therefore, the model is required to capture long-range dependencies. For better evaluating PAIE and comparing with others, we list their performance breakdown on different sentence distances between arguments and the given trigger word in Table 5. We can see that (1) PAIE significantly improves the ability to extract arguments with long distances, especially for those behind the trigger words (see columns with positive d values). (2) The last two rows of the table indicate that joint prompts in PAIE leverage the implicit interaction among roles, and roles conditioning on each other lower the difficulty to extract long-distance arguments effectively. Multiple arguments may share the same role in the same event. We show that PAIE outperforms QA-based models dealing with it in both efficiency and effectiveness in this section. Efficiency To solve this problem, QA-based methods usually adopt the thresholding strategy, which PAIE 30 40 50 60 0.0 2.5 5.0 7.5 10.0 threshold A r g  C sc o r e EEQA EEQABART Figure 4: Arg-C F1 w.r.t different thresholds for WIKIEVENTS. compares the score of each text span with a manually tuned threshold. We claim that it consumes lots of time and computational resources for finding a good threshold and usually ends with sub-optimal results. We support such claim by a coarse grid search tuning span threshold on WIKIEVENTS dataset using EEQA and EEQA-BART models, as shown in Fig. 4. The choice of threshold highly affects the performance of the model. In addition, models with the same architecture but different PLMs have totally different optimal thresholds even on the same dataset, not to mention on distinct datasets. PAIE requires no threshold tuning since each slot in the prompt only predicts at most one argument span and usually achieves much higher inference speed in practice. Effectiveness We also compare the capability of PAIE and EEQA-BART in predicting multiple arguments with the same role on WIKIEVENTS, a dataset containing diverse multi-argument cases. Table 6 shows that PAIE outperforms significantly better than EEQA-BART dealing with such cases. For roles with three and four or more arguments, PAIE gains a definite Arg-C F1 improvement of 9 . 5% and 26 . 4% , respectively. We analyze how PAIE performs under a scenario without sufficient annotations. Fig. 5 shows the performance of PAIE and two other QA-based baselines with partial training samples on three benchmarks. It demonstrates that (1) PAIE is superior to EEQA-BART and EEQA in almost all settings with different datasets and training data ratios. (2) PAIE especially outperforms QA-based methods in document-level tasks (RAMS and WIKIEVENTS). It achieves comparable F1 scores with EEQA-BART using only about 20% training samples and EEQA using about 10% samples. (3) Along with the decreasing number of training data, the gains become larger than baselines. All observations above indicate that PAIE can better utilize PLMs for few-shot settings. Most of the previous sections emphasize the superiority of PAIE from the perspective of accuracy performance. Actually, PAIE also has much better extraction efficiency compared with other approaches. In Table 7, we report the overall inference time for different models. PAIE usually runs 3-4 times faster than EEQA, since it predicts multiple roles simultaneously, while EEQA predicts roles one by one. Other QA-based models are likely to have similar speeds with EEQA due to their sequential Model ACE05 RAMS WIKI B L B L B L BART-Gen 5.8 12.4 33.2 54.8 19.1 29.0 EEQA-BART 11.8 36.0 66.0 187.4 30.9 83.8 PAIE 2.9 8.4 19.0 38.6 8.4 18.3 Table 7: Inference time (second) for different models on test set of ACE05, RAMS, WIKIEVENTS. prediction structure and training process. Also, as discussed in Section 6.2, PAIE is even more advantageous under practical application scenarios since it avoids the heavy threshold tuning. We propose a novel model PAIE that effectively and efficiently extracts arguments at both sentence and document levels. We define a new prompt tuning paradigm for extraction tasks which prompts multiple role knowledge from PLMs via role-specific selectors and joint prompts. Extensive experiments on three standard benchmarks demonstrate our proposed model's effectiveness and the generalization ability in both sentence and document level EAE. We have also conducted ablation studies on the main components, the extractive prompting strategy, and several real scenarios. In the future, we are interested in investigating co-reference as an auxiliary task of EAE and introducing entity information to better determine argument boundaries. This study is supported under the RIE2020 Industry Alignment Fund  Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s). We also thank the KULeuven C1 project Macchina for support.
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