P rompt for Extraction? PAIE : P rompting Argument Interaction for Event Argument Extraction (2022)

Abstract: 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 .

## introduction understanding text by identifying the event and arguments has been a long-standing goal in natural language processing (nlp) @xcite . 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 dan sanchez reports : the saudis go full isis in their us -backed takfiri war on the shia saudi arabia has perpetrated a @xmath0. 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 @xmath1, its universal theme parks and television production company. recent years @xcite , the main challenge lies in event argument extraction @xcite . ## methodology 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. ## experiments in this section, we explore the following questions: model plm ace05 rams wikievents arg-i arg-c arg-i arg-c arg-i arg-c head-c feae @xcite bert-b --53.5* 47.4* ---docmrc @xcite bert ## conclusion 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. hang yang, dianbo sui, yubo chen, kang liu, jun zhao, and taifeng wang. 2021. document-level event extraction via parallel prediction networks. in proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pages 6298-6308, online. association for computational linguistics. shun zheng, wei cao, wei xu, and jiang bian. 2019. doc2edag: an end-to-end document-level framework for chinese financial event extraction. in proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (emnlp-ijcnlp), pages 337-346, hong kong, china. association for computational linguistics.