How Relevant Are Selectional Preferences for Transformer-based Language Models?

Proceedings of the 28th International Conference on Computational Linguistics, pages 114–124 Barcelona, Spain (Online), December 8-13, 2020 114 Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection Disha Jindal†, Daniel Deutsch, Dan Roth Department of Computer and Information Science, University of Pennsylvania {djjindal, ddeutsch, danroth}@seas.upenn.edu Abstract Identifying the key events in a document is critical to holistically understanding its important information. Although measuring the salience of events is highly contextual, most previous work has used a limited representation of events that omits essential information. In this work, we propose a highly contextual model of event salience that uses a rich representation of events, incorporates document-level information and allows for interactions between latent event encodings. Our experimental results on an event salience dataset (Liu et al., 2018) demonstrate that our model improves over previous work by an absolute 2-4% on standard metrics, establishing a new state-of-the-art performance for the task. We also propose a new evaluation metric which addresses flaws in previous evaluation methodologies. Finally, we discuss the importance of salient event detection for the downstream task of summarization.1 1 Introduction Identifying the salient information in a given piece of text is a ubiquitous and important problem in natural language understanding. While important parts of the text have been identified by attending to entities (Dunietz and Gillick, 2014), elementary discourse units (Xu et al., 2020), or whole sentences (Zhou et al., 2018; Liu and Lapata, 2019), in this work, we choose to model extracting important events (Liu et al., 2018; Choubey et al., 2018). Events are the core parts of most sentences – they center around a predicate and include its key arguments – yet they are compact semantic units, and a salient event in a sentence could carry the sentence’s meaning efficiently. Extracting important events has been shown to be central to many downstream tasks, such as summarization (Marujo, 2015), storyline creation (Martin et al., 2018) and question answering (Kocisk´y et al., 2018). To model the importance of an event, it is critical to understand its context: who is involved, where did it happen, what other events is it related to, and more. For instance, in Figure 1, the difference in salience of the “fled” events in each document is significantly influenced by their arguments (“2,100 Colombians” versus “shopkeepers”). Previous work that identifies salient events has a limited event representation that is unable to capture these important contextual signals (Liu et al., 2018; Choubey et al., 2018). In contrast, the model which we propose in this work (§3) directly models the context of an event in three different ways as follows. First, instead of representing an event by only its predicate mention, our representation includes the subject, object, time, and location of the event (§3.1). Second, we directly incorporate global features into the model that capture hierarchical relations between events, abstract event frames, position information, and more (§3.2). Third, we use a neural network architecture that includes an inter-event interaction layer, which allows information to be …

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