Event Argument Extraction using Causal Knowledge Structures (2020)

Abstract: Event Argument extraction refers to the task of extracting structured information from unstructured text for a particular event of interest. The existing works exhibit poor capabilities to extract causal event arguments like Reason and After Effects. Futhermore, most of the existing works model this task at a sentence level, restricting the context to a local scope. While it may be effective for short spans of text, for longer bodies of text such as news articles, it has often been observed that the arguments for an event do not necessarily occur in the same sentence as that containing an event trigger. To tackle the issue of argument scattering across sentences, the use of global context becomes imperative in this task. In our work, we propose an external knowledge aided approach to infuse document level event information to aid the extraction of complex event arguments. We develop a causal network for our event-annotated dataset by extracting relevant event causal structures from ConceptNet and phrases from Wikipedia. We use the extracted event causal features in a bi-directional transformer encoder to effectively capture long-range inter-sentence dependencies. We report the effectiveness of our proposed approach through both qualitative and quantitative analysis. In this task, we establish our findings on an event annotated dataset in 5 Indian languages. This dataset adds further complexity to the task by labeling arguments of entity type (like Time, Place) as well as more complex argument types (like Reason, After-Effect). Our approach achieves state-of-the-art performance across all the five languages. Since our work does not rely on any language specific features, it can be easily extended to other languages as well.

## introduction event argument extraction is a key information extraction task that extracts structured information from unstructured texts. a widely studied task, it often involves multiple complex sub-tasks like entity retrieval, trigger detection and evententity linking. in almost all the existing works, the terms event arguments and entities have been used interchangeably. popular entity types which are frequently mined are that of place, time, organisations, and persons of interest. our data set contains arguments of entity type, like time and place along with more complex argument types, like after effects and reason. these arguments with non-entity type attributes have comparatively much fewer instances in the data set than the entity type arguments. not only that, these arguments also constitute a much more complex lexical and syntactical structure and are hard to capture using the existing architectures. in our work, we propose a novel event feature that helps capture rare complex argument instances more accurately than the existing models on this task. we construct a causal knowledge structure with the aid of external knowledge bases and for a given event, we extract its corresponding causal attribute from the structure. context has always been of paramount importance for the task of event argument extraction. existing literature outlines different paradigms of modeling the contextual scope for this task. the traditional sentence level event argument extraction tasks performed on popular datasets like ace 2005 and tac-kbp 2017 largely restrict their contextual scope to within sentence boundaries @xcite . however, some researchers identified the need to mine global contextual features to enhance the argument extraction capabilities and modelled the task at a multisentence or paragraph level @xcite @xcite , and at a document level @xcite . in our work, we also identify the need to explore cross-sentence contextual scope to aid accurate extraction of events and argument spans. for example as illustrated in figure 1 , the sentence "the brahmaputra is flowing above the danger mark in six districts." could refer to a reason or an after-effect argument in different contexts. but on reading the document, one can understand that the document talks about a flood event, and that the sentence can be tagged as reason argument with considerable confidence. event arguments do not necessarily co-occur in the sentence containing the event trigger. this behaviour is even more frequently observed in longer documents like news articles. with no thematic signals obtained from event trigger words, it becomes difficult to identify the correct argument labels and their spans in such cases. in our work, we look beyond sentences for stronger contextual clues to better capture the events and their arguments from a document. in summary, the contributions of our work are four fold : ## related work event argument extraction is a well researched domain, primarily in english @xcite , @xcite and chinese @xcite , @xcite . while there have been efforts to extend this task to a limited number of languages like spanish and arabic @xcite , @xcite , the task has been hardly explored in indian languages. in this paper, we make an effort to explore five out of the twenty two commonly spoken languages of india. the existing literature for indian languages for this task are minimal and have either extracted arguments from unstructured text irrespective of their event links @xcite , or have jointly extracted event and argument mentions with rule based approaches @xcite . each of these works are modelled to focus at an intra-sentence local scope and do not capture inter sentence dependencies. while the sentence level extraction mechanisms outlined by prior works present a promising preface for this task in low-resource languages, the results are indicative of a huge scope of improvement -especially for arguments like reason which are scant on annotations. in our work, we try to address this gap and extract sentence level events and arguments by infusing document level context. infusing cross-sentence or global context to extract sentence level information has already shown great improvements in many related works. the work by @xcite studies the eventargument extraction task by infusing contextual information from the five sentences surrounding an event trigger. their work highlights the fact that learnt architectures usually capture arguments from sentences containing event triggers better than from non-event-trigger sentences. addressing this gap, we adopt a trigger-less event detection approach in our work, similar to @xcite and use document-level event information to mitigate the dependence on local event clues obtained from event triggers. the method adopted by @xcite uses a graphical span enumeration approach with a document input to model cross-task and inter-sentence dependencies. we model our work to include inter-sentential context to extract event-argument spans from input documents with greater accuracy. ## dataset we have curated an event argument annotated dataset for english and four indian languages, namely bengali, hindi, marathi and tamil as part of a collaborative effort. repository as well as by crawling popular news websites for the respective languages. the dataset was annotated by eight linguistic experts over a span of two years. the multi-rater kappa agreement ratio of the annotators has been evaluated as 0.85 approximately. the data distribution is reported in in this section we elaborate on the approaches we have taken to accomplish the task of extracting the event labels and it's corresponding arguments from a document. this is essentially a two-fold task: i) document level event extraction, and ii) argument extraction. the generic overview of our approach is illustrated in figure 2 . ## analysis we perform a thorough analysis of our approaches and enlist their merits and demerits in this section. we compare our models to the state-of-the-art models established on this dataset. as reported in we show the % increase (+) or decrease (-) in f-scores with respect to the sota results. in the example, it can be observed that the model confuses the event trigger due to landslide as the reason for the death of two workers. although, it is correct in it's essence, as per the task, the phrase due to landslide is an event trigger and not part of a reason argument. ## conclusion in our work we have shown a pipeline approach to mine document level event labels and their arguments from each sentence in a document. we have reported a comparison study across different approaches based on multiple parameters, like input context and event information. in future, we want to extend this task to handle more languages in a multilingual setting. our approaches have shown a huge improvement over the state-of-the art methods. in conclusion, through our work, we emphasize on the importance of both contextual and event information and report our thorough investigations on the same.