Incorporating Circumstances into Narrative Event Prediction (2021.findings-emnlp)
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| Challenge: | Existing studies focus on mining the inter-events relationships while ignoring how the events happened. |
| Approach: | They propose to incorporate event circumstances into the narrative event prediction by combining two multi-head attention modules and regularizing attention weights. |
| Outcome: | The proposed model outperforms baseline models by 12.2%. |
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| Challenge: | a novel approach to narrative event representation uses attention to re-contextualize events across the whole story . a recent study shows that attention is used to attach event semantics to tokens . |
| Approach: | They propose an unsupervised approach to narrative event representation using attention to re-contextualize events across the whole story. |
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NGEP: A Graph-based Event Planning Framework for Story Generation (2022.aacl-short)
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| Challenge: | Current approaches to story generation are based on end-to-end neural generation models, such as BART, to generate event sequences. |
| Approach: | They propose a novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph and enhances generalisation ability through a neural event advisor. |
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Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts (2022.naacl-main)
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| Challenge: | Existing systems that generate *flashbacks* are monotonic and lack explicit guidance on how to insert them. |
| Approach: | They propose to use event temporal orders to encode events as temporal prompts . they leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines . |
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Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
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COMET-M: Reasoning about Multiple Events in Complex Sentences (2023.findings-emnlp)
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| Challenge: | Existing commonsense models that generate event-centric inferences for simple sentences struggle with the complexity of multi-event sentences prevalent in natural text. |
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A Causal Approach for Counterfactual Reasoning in Narratives (2024.acl-long)
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| Challenge: | Existing methods for counterfactual reasoning in narratives are based on dataset-specific heuristics, but they are abusing unique patterns, i.e., the feature of minimum editing, in the dataset, which limits the generality of their methods. |
| Approach: | They propose a basic VAE module for counterfactual reasoning in narratives and introduce a pre-trained classifier and external event commonsense to mitigate the posterior collapse problem. |
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Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022.coling-1)
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| Challenge: | Existing methods to predict event sequences are complex and ignore the knowledge of external events. |
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EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention (2022.findings-emnlp)
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| Challenge: | Existing methods for story generation still suffer from problems of relevance and coherence. |
| Approach: | They propose a novel neural generation model which maps contextual and event features to event sequences with a cross-attention mechanism and exploits logical relatedness between events. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic and human evaluations and shows that it can leverage contextual and event features. |
Salience-Aware Event Chain Modeling for Narrative Understanding (2021.emnlp-main)
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| Challenge: | Storytelling is the communication of interesting and related events that form a concrete process. |
| Approach: | They propose methods for extracting the principal chain from natural language text . they filter away non-salient events and supportive sentences to isolate them . authors propose novel methods for predicting and answering events from text based on event-based temporal question answering . |
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A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)
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| Challenge: | Existing approaches to modeling media narratives miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. |
| Approach: | They propose a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. |
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