“Killing Me” Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism (2021)

Abstract: Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context words are important for detecting spoilers, current attention-based spoiler detection models are insufficient for utilizing dependency relations. To address this problem, we propose a new spoiler detection model called SDGNN that is based on syntax-aware graph neural networks. In the experiments on two real-world benchmark datasets, we show that our SDGNN outperforms the existing spoiler detection models.

## introduction spoilers on review websites, which reveal critical details of the original works, can ruin an appreciation for the works. review websites, such as rotten tomato, imdb, and metacritic, provide self-reporting systems that tag spoiler information to warn users of spoilers. however, since selfreporting systems depend solely on the active participation of users, they cannot handle the fastgrowing volume of newly generated reviews. during the past decade, several machine learning-based spoiler detection (sd) models have been proposed to solve the inefficiency of self-reporting systems. @xcite proposed an automatic sd model that measures the similarity between reviews and synopses of movies. support vector machine (svm)-based sd models using handcrafted features have been proposed @xcite . recently, attention-based sd models that utilize metadata of review documents achieve state-of-the-art performance on the sd task @xcite . however, the attention-based sd models have a lack of using dependency relations between context words. dependency relations are useful for capturing the semantics of given sentences and detecting spoilers. as shown in figure 1 , although the phrase "killing me" is not a spoiler because the phrase is a metaphor, the attention-based sd models often focus on the word "killing" and classify sentences that contain the phrase "killing me" as spoilers. by providing the information that the word "me" is used as the direct object of the verb "killing," sd models can understand that the phrase is a metaphor. in this paper, we propose sdgnn, which is a new spoiler detection model based on syntaxaware graph neural networks (gnns) @xcite for leveraging dependency relations between context words in sentences to fully capture the semantics. with the success of gnns we also propose a dependency relationaware attention mechanism, which is a modification of the gating mechanism used by syntaxaware gnns, to be suitable for the spoiler detection task. in sd, considering the relative importance of dependency relations. however, existing syntaxaware gnn-based models compute the importance of each dependency relation individually in sentences without considering the context of the given sentence. our proposed dependency relation-aware attention mechanism considers the relative importance of dependency relations. also, we adopt a previously proposed genre-aware pooling method @xcite to utilize the genre of works efficiently. in the experiments, we demonstrate the effectiveness of sdgnn on two real-world benchmark datasets in both quantitative and qualitative ways. ## our approach sdgnn classifies whether a given sentence @xmath0, w 2 , • • • , w n ) is a spoiler sentence. sdgnn consists of three stages: contextualized word representation, dependency relation-aware attention mechanism, and genre-aware pooling. contextualized word representation each word w in the given sentence x is represented with the pretrained word embedding vector @xcite . we then utilize bi-directional lstms @xcite to encode contextualized word representations h (0) ∈ r d . ## conclusion in this paper, we proposed a novel spoiler detection model called sdgnn which is based on syntaxaware gnns that utilize dependency relations between context words. we also proposed a dependency relation-aware attention mechanism for considering the relative importance of dependency relations. in the experiments, our proposed sdgnn model achieved the state-of-the-art performance on two spoiler datasets. our experimental results demonstrate the effectiveness of dependency relations in the spoiler detection task and our dependency relation-aware attention mechanism.