A rg G en: Prompting Text Generation Models for Document-Level Event-Argument Aggregation (2022)

Abstract: Most of the existing discourse-level Information Extraction tasks have been modeled to be extractive in nature. However, we argue that extracting information from larger bodies of discourse-like documents requires more natural language understanding and reasoning capabilities. In our work, we propose the novel task of document-level event argument aggregation which generates consolidated event-arguments at a document-level with minimal loss of information. More specifically, we focus on generating precise document-level information frames in a multilingual setting using prompt-based methods. In this paper, we show the effectiveness of u prompt-based text generation approach to generate document-level argument spans in a low-resource and zero-shot setting. We also release the first of its kind multilingual event argument aggregation dataset that can be leveraged in other related multilingual text generation tasks as well: https://github.com/DebanjanaKar/ArgGen .

## introduction discourse-based information extraction (ie)is a well-explored nlp task. most of these works @xcite rely on extractive approaches to mine relevant eventargument spans for specific argument roles. however, there are two main challenges in this effort. first, extractive argument spans may miss implicit information at a document-level. for example in figure 1 , the time mentions in the document include the publishing date of the document and the day of the week the event occurred. an extractive approach will not be able to accurately determine the date of the event. we aim to address this challenge using a conditional text generation approach. second, sentence-level argument mentions in the document are often scattered and may contain similar yet distinct information. for example, the casualties argument mentions like 'kill 37', 'at least 37 civilians', 'killed several people, including militants' in the example (figure 1 ) contain repetitive but slightly distinct information. an extractive method extracting such document level arguments may again miss key information as they employ elimination strategies to select the key argument mention at the document level. the approach we propose addresses this challenge by leveraging argument specific prompts with conditional text generation methods. in this paper, we provide a fresh perspective to discourse-based ie and propose the task of event argument aggregation. event argument aggregation is a challenging natural language understanding task that aims to consolidate document-level structured information from given unstructured text. closely related to the task of document-level event argument extraction, event argument aggregation emphasizes on filtering redundant and irrelevant argument mentions to generate precise documentlevel information frames. in our work, we focus on producing the document-level information frames using prompt-based generative approaches. in our work, we adopt @xcite 's idea of reducing our related task of document-level event argument aggregation to that of natural language question answering. a very recently published work related to the task of event argument generation is that of @xcite . like our approach, they too employ conditioned text generation to generate document-level event arguments. however, the argument spans they extract at a document level are much shorter and explicit in nature than our argument mentions. prompt-based methods have recently gained popularity in a number of related tasks like entity extraction @xcite , question answering @xcite ) and text generation @xcite . in this paper, we show the effectiveness of promptbased methods to aggregate event-arguments at a document-level. we evaluate our models on lowresource settings as well as more challenging zeroshot settings. we discuss and analyse the effectiveness of the proposed model in the following sections. the key contributions of our work are enumerated as follows: i) we propose a fresh perspective to discourse-based ie through our proposed task of event argument aggregation. ii) we are the first to explore prompt-based conditional text generation to aggregate event-arguments at a document level. our proposed model provides state-of-the-art results on this task. iii) we are the first to release an annotated, multilingual event-argument aggregation dataset. the corpus consists of 346 annotated documents in english, hindi and bengali. ## event-argument aggregation in this section, we detail the approaches we propose for the task of document-level event argument aggregation. the framework primarily involves three steps: i) mrc pre-training, ii) prompt engineering, iii) qa-based argument generation. ## dataset in the sections to follow, we discuss the details of the datasets we created for i) the mrc pretraining task and ii) multilingual event argument aggregation (arggen dataset). ## discussion we have used mt5-base foot_2 @xcite model at the core of our experiments. in ## conclusion we have presented arggen, a low-resource, prompt-based multilingual framework which aggregates event argument mentions at a document-level. we have also presented a fresh perspective in the domain of multilingual ie through our proposed challenging task of document-level event argument aggregation. we provide access to a novel multilingual event argument aggregation dataset which can also be leveraged for other related natural language generation tasks: https://github. com/debanjanakar/arggen . our proposed model not only generates syntactically and semantically relevant aggregated argument mentions but demonstrates similar effectiveness in a zero-shot setting as well. in the future, we want to explore this task across more languages and documents.