Event extraction is a crucial task for semantic understanding and structured knowledge construction. However, the expense of collecting and labeling data for training event extraction models is usually high. To address this issue, we propose a novel schema-based data augmentation method that utilizes event schemas to guide the data generation process. The event schemas depict the typical patterns of complex events and can be used to create new synthetic data for event extraction. Specifically, we sub-sample from the schema graph to obtain a subgraph, instantiate the schema subgraph, and then convert the instantiated subgraph to natural language texts. We conduct extensive experiments on event trigger detection, event trigger extraction, and event argument extraction tasks using two datasets (including five scenarios). The experimental results demonstrate that our proposed data-augmentation method produces high-quality generated data and significantly enhances the model performance, with up to 12% increase in F1 score compared to baseline methods.
Large language models (LLM’s) have been widely used for several applications such as question answering, text classification and clustering. While the preliminary results across the aforementioned tasks looks promising, recent work has dived deep into LLM’s performing poorly for complex Named Entity Recognition (NER) tasks in comparison to fine-tuned pre-trained language models (PLM’s). To enhance wider adoption of LLM’s, our paper investigates the robustness of such LLM NER models and its instruction fine-tuned variants to adversarial attacks. In particular, we propose a novel attack which relies on disentanglement and word attribution techniques where the former aids in learning an embedding capturing both entity and non-entity influences separately, and the latter aids in identifying important words across both components. This is in stark contrast to most techniques which primarily leverage non-entity words for perturbations limiting the space being explored to synthesize effective adversarial examples. Adversarial training results based on our method improves the F1 score over original LLM NER model by 8% and 18% on CoNLL-2003 and Ontonotes 5.0 datasets respectively.
Event-event temporal relation extraction aims to extract the temporal order between a pair of event mentions, which is usually used to construct temporal event graphs. However, event graphs generated by existing methods are usually globally inconsistent (event graphs containing cycles), semantically irrelevant (two unrelated events having temporal links), and context unaware (neglecting neighborhood information of an event node). In this paper, we propose a novel event-event temporal relation extraction method to address these limitations. Our model combines a pretrained language model and a graph neural network to output event embeddings, which captures the contextual information of event graphs. Moreover, to achieve global consistency and semantic relevance, (1) event temporal order should be in accordance with the norm of their embeddings, and (2) two events have temporal relation only if their embeddings are close enough. Experimental results on a real-world event dataset demonstrate that our method achieves state-of-the-art performance and generates high-quality event graphs.
Event schema depicts the typical structure of complex events, serving as a scaffolding to effectively analyze, predict, and possibly intervene in the ongoing events. To induce event schemas from historical events, previous work uses an event-by-event scheme, ignoring the global structure of the entire schema graph. We propose a new event schema induction framework using double graph autoencoders, which captures the global dependencies among nodes in event graphs. Specifically, we first extract the event skeleton from an event graph and design a variational directed acyclic graph (DAG) autoencoder to learn its global structure. Then we further fill in the event arguments for the skeleton, and use another Graph Convolutional Network (GCN) based autoencoder to reconstruct entity-entity relations as well as to detect coreferential entities. By performing this two-stage induction decomposition, the model can avoid reconstructing the entire graph in one step, allowing it to focus on learning global structures between events. Experimental results on three event graph datasets demonstrate that our method achieves state-of-the-art performance and induces high-quality event schemas with global consistency.