Trigger-Argument based Explanation for Event Detection (2023)

Abstract: Event Detection (ED) is a critical task that aims to identify events of certain types in plain text. Neural models have achieved great success on ED, thus coming with a desire for higher interpretability. Existing works mainly exploit words or phrases of the input text to explain models’ inner mechanisms. However, for ED, the event structure, comprising of an event trigger and a set of arguments, are more enlightening clues to explain model behaviors. To this end, we propose a Trigger-Argument based Explanation method (TAE), which can utilize event structure knowledge to uncover a faithful interpretation for the existing ED models at neuron level. Specifically, we design group, sparsity, support mechanisms to construct the event structure from structuralization, compactness, and faithfulness perspectives. We evaluate our model on the large-scale MAVEN and the widely-used ACE 2005 datasets, and observe that TAE is able to reveal the process by which the model predicts. Experimental results also demonstrate that TAE can not only improve the interpretability on standard evaluation metrics, but also effectively facilitate the human understanding.

## introduction event detection (ed) aims at identifying event triggers with specific event types, which is the first and fundamental step for extracting semantic and structural knowledge from plain text @xcite . for instance, event mention "the train driver was beaten over the head by a thug." in figure 1 comprises an event trigger "beaten" and a set of arguments such as "the train driver", "the head" and "a thug". an ideal ed system is expected to detect "beaten" as an event trigger of the type bodily_harm. recently, with the growth of open source annotated datasets train the driver was beaten over the head by a thug ( @xcite and the development of deep learning technologies, deep approaches have become popular for tackling the ed problem @xcite . despite their great performance, they are still opaque for people to comprehend the inner mechanisms. although there exist many works that focus on explaining the model behavior on natural language processing (nlp) problems, such as text classification @xcite ), text matching @xcite and machine reading comprehension @xcite , very little progress has been made to interpret ed models. we identify two major limitations that prevent the existing explanation methods from being applied to ed models. neglecting event structured knowledge. existing methods mainly focus on assessing the contributions of individual input unit (e.g., word or phrase) to generate explanations for neural networks @xcite . as shown in figure 1(a ) and (b), both explanations provide insights of which words (e.g., "beaten") or phrases (e.g., "beaten over the head") contribute to the prediction. however, neither of them is suitable to explain ed models as an event is represented as a structure comprising an event trigger and a set of arguments. thus, the trigger-argument structures are more sensible clues to explain ed systems. in figure 1(c ), "beaten" is an ambiguous word that may evoke completely dissimilar events such as bodily_harm and competition. in this case, trigger word "beaten" and its arguments (e.g., "the head" and "a thug" which refer to body_part and agent) work together for the prediction bodily_harm. thus, how to take advantage of the event structure knowledge for ed model explanation is a non-trivial task. explanations cannot reflect the decisionmaking process. models usually provide important features which are words or phrases selected from an input text as explanations, but they do not further elaborate the function of these features, i.e., why models produce the prediction according to these features. it poses challenges to interpret an explanation and connect it to model prediction. for example, in figure 1(a ) and (b), models may assign high relevance score to "train driver" or "thug", but it is still confused why these features can lead to the prediction bodily_harm. in fact, "train driver" and "thug" serve as victim and agent, which compose together for the bodily_harm event in which "an agent injures a victim" in figure 1(c ). furthermore, figure 1 (d) provides an example that wrongly classifies competition as bodily_harm, because models take "harley" and "john" as agent and victim rather than participant_1 and participant_2. thus, exploring explanations that can not only identify important features but also reveal how these features contribute to the prediction are urgently needed. to address the aforementioned challenges, we propose tae, a trigger-argument based explanation method, to generate structure level explanations for ed models. specifically, tae focuses on utilizing neuron features of ed models to construct explanations based on trigger-argument knowledge. it has three core sub-modules: group modeling aims to divide neurons into different groups, where each group is regarded as an event structure, in such a way that each neuron corresponds to one argument and works together with other neurons that belong to the same event structure to explain the prediction of ed models; sparsity modeling aims to compact explanations by designing differentiable masking mechanisms to automatically filter out useless features generated by the group mechanism, and the intuition behind this module is that a good explanation should be short for understanding or reading @xcite ; support modeling aims to ensure that the explanations generated by the group and sparsity mechanisms are faithful to the predictive model. note we utilize framenet, a well-defined linguistic knowledge base by experts, to assist tae identify event structures and help humans understand the decision-making process. the contributions of this paper are as follows: in this section, we review the related works on event detection and interpretation methods. ## method in this paper, we propose tae, a trigger-argument based explanation method for event detection, which attempts to utilize event structure knowledge to explain model predictions at neuron level. the overview of our method is shown in figure 2 , which contains three modules: (1) the group module captures structured knowledge of events; the loss function of an structured explanation for an event is obtained by an optimization problem: ## conclusion in this paper, we propose a trigger-argument based explanation method, tae, which exploits the event structure-level explanations for event detection (ed) task. tae focuses on utilizing neuron features of ed models to generate explanations, along with three strategies, namely, group modeling, sparsity modeling, and support modeling. we conduct experiments on two ed datasets (i.e., maven and ace). the results show that tae achieves better performance compared to the strong baselines on multiple public metrics. in addition, tae also provides more faithful and human-understandable explanations. there might be a few different future directions. firstly, we might look into the idea of using explanations to further improve ed classification, as well as ed explanations in downstream applications. secondly, we plan to explore ed under the multi-modal setting. thirdly, event relation extraction is still challenging and deserves some further investigation. from the practical aspect of event knowledge graphs (ekgs), it is worth investigating high-quality yet efficient methods for constructing ekgs and making use of ekgs to predict future events @xcite . furthermore, it might be an idea to integrate commonsense knowledge @xcite @xcite into event knowledge graphs.