Salience-Aware Event Chain Modeling for Narrative Understanding (2021.emnlp-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method improves narrative prediction and event-based temporal question answering tasks. |
Similar Papers
Event-Centric Natural Language Processing (2021.acl-tutorials)
Copied to clipboard
| 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. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
Copied to clipboard
| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
| Outcome: | The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data. |
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020.coling-main)
Copied to clipboard
| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
| Approach: | They propose to use Barthes’ definition of event salience to compute event salientity without annotations by using a pre-trained language model. |
| Outcome: | The proposed methods outperform baseline methods on folktales with event salience annotation and fine-tuned language model is key factor in improving the methods. |
Narrative Embedding: Re-Contextualization Through Attention (2021.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed approach achieves state of the art performance on multiple choice and story cloze tasks. |
Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for EE depend on manual annotations, which are expensive and scarce. |
| Approach: | They propose to transform the event extraction task into multi-turn dialogues and a novel method for generating high-quality data. |
| Outcome: | The proposed methods significantly improve existing models’ performance with various paradigms and structures, especially on tail types. |
Narrative Modeling with Memory Chains and Semantic Supervision (P18-2)
Copied to clipboard
| Challenge: | Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. |
| Approach: | They propose a method that tracks various semantic aspects with external neural memory chains . they propose to encourage each to focus on a particular semantic aspect . |
| Outcome: | The proposed method outperforms baselines on the task of story ending prediction. |
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing event-centric knowledge graphs rely on explicit connectives to extract relations between events. |
| Approach: | They propose a knowledge projection paradigm for event relation extraction using commonalities between events. |
| Outcome: | The proposed method achieves state-of-the-art performance and extrinsic results verify the extracted event relations. |
A Structured Clustering Approach for Inducing Media Narratives (2026.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation. |
Towards Layered Events and Schema Representations in Long Documents (2021.naacl-srw)
Copied to clipboard
| Challenge: | a thesis aims to explore the use of event extraction in literary texts . event extraction is a challenging domain based on its variety of genres . |
| Approach: | They propose to use event extraction to extract semantic information from literary texts . they propose to build on sequences of event embeddings to form schema embeddables . |
| Outcome: | The proposed approach will allow comparisons between sections of documents and entire literary works. |
Exploring Sentence Community for Document-Level Event Extraction (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to document-level event extraction neglect the complex logic structures in long texts. |
| Approach: | They propose a framework that exploits the relationship between sentences to extract multiple events by sentence community detection using graph attention networks. |
| Outcome: | The proposed framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset. |