Joint Modeling of Arguments for Event Understanding (2020)

Abstract: We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots. The approach allows for joint consideration of argument candidates given a detected event, which we illustrate leads to state-of-the-art performance in multi-sentence argument linking.

## introduction given an event recognized in text, we are concerned with finding its associated arguments. significant work has focused at the level of single sentence contexts, such as in semantic role labeling (srl; @xcite @xcite . unfortunately even perfect performance in srl will be limited by the existence of arguments outside the sentence boundary, leading to prior work @xcite @xcite ) on an alternative paradigm variously called implicit role resolution or argument linking, where an event trigger (e.g. "attack") evokes a set of roles (e.g. at-tacker, target) to be filled, and they are linked to explicit argument mentions found in text. in argument linking, possible candidate arguments are first detected, then linked to specific roles of detected events. this bears similarity to coreference resolution, where document-level context can be aptly utilized. for an example, see figure 1 . this formulation is similar to the resolution of referring expressions in conversational dialogues @xcite , where a current utterance is considered to invoke an intent (e.g. buy-book), accompanied by a number of slots (e.g. 1 our code can be found at https://github.com/ wanmok/joint-arglinking . ## approach argument and trigger representation we compute a fixed-length vector with dimension for each argument and trigger span as their representations. to compute this, we first pass the document through a pre-trained contextualizing model (bert @xcite here). 5 we split documents into sentences and feed each sentence to bert for encoding. each token might be split into more than 1 subword units-in this case we take the average of these subword representations so that each token has 1 vector representation @xmath0, following @xcite . for an argument @xmath1, • • • , ), we follow @xcite to generate a span embedding. 6 the span embedding m for mention span comprises of three parts, the representation of its left boundary, its right boundary, and a learned pooling over the tokens in the span. this learned pooling utilized a global attention query vector @xmath2, and computes the weighted sum of all tokens with respect to the attention scores derived from q: it showed footage of ambulances arriving at the kilis state hospital and medical personnel unloading children on stretchers and a girl wrapped in a blanket , as well as a handful of adults. the observatory and al -halaby also reported an air raid on the village of kaljibrin near azaz . and pass that through a 2-layer feed-forward neural network to yield a fixed-length vector @xmath3: similarly, for any trigger @xmath4: ], we employ a different set of parameters: ## experiments as we draw the connections between slu in dialogue systems and argument linking in information extraction, we focus primarily on evaluating the model a discourse-level dataset, rams @xcite . first however we look at a more established dataset, ace 2005 (walker et al., 2006) foot_5 , to verify if our model can reasonable performance compared to prior work in event understanding. while ace 2005 is annotated only at the sentencelevel, our model may still be applied in this setting. for detailed experimental setup, see appendix a. baseline aside from joint modeling of arguments, we also include an independent model as a case in ablation studies (while our proposed method labeled as joint). the independent model removes the transformer encoder (cf. equation 4 ), but directly applies a feed-forward neural network atop of the trigger representation and each argument representation to classify the role (or absence) of the argument with respect to the event trigger. metrics we use precision, recall, and f 1 -score as metrics. a link between the trigger and an argument is considered correct, if and only if the predicted argument span offsets and role matches the gold reference. we report using micro-average among f 1 -scores across different roles. ## 6 conclusion we proposed a joint modeling approach for argument linking that considers the interdependent relationships among argument mentions conditioning on a specific event. our approach extends from recent work in dialogue systems, viewing a document as essentially a single-side discourse, and where event arguments are recognized as similar to slots that potentially carryover across utterances. experimental results show our approach achieves superior performance on a recently introduced dataset for modeling discourse-level contexts.