Augmentation, Retrieval, Generation: Event Sequence Prediction with a Three-Stage Sequence-to-Sequence Approach (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to predict event sequences are complex and ignore the knowledge of external events. |
| Approach: | They propose a statistical induction problem to generate a sequence of events by exploring the similarity between the given goal and known sequences of events. |
| Outcome: | The proposed model outperforms existing methods on an event sequence prediction task. |
Similar Papers
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
Generating Temporally-ordered Event Sequences via Event Optimal Transport (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for temporal event ordering and event infilling ignore the global semantics of events, and the model adopts a word-level objective to model events in texts. |
| Approach: | They propose a temporal event ordering and event infilling task using a model that uses maximum likelihood estimation to model events in texts. |
| Outcome: | The proposed model outperforms existing models on all evaluation datasets. |
Don’t Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models (2021.acl-short)
Copied to clipboard
| Challenge: | Existing approaches to train event language models on text constrain them to follow discourse order of events. |
| Approach: | They propose a method to perturb event sequences so that they can relax model dependence on text order. |
| Outcome: | The proposed technique improves performance on applications and out-of-domain events data. |
Schema-based Data Augmentation for Event Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing data augmentation methods rely on language models to train event extraction models. |
| Approach: | They propose a schema-based data augmentation method that utilizes event schemas to guide the data generation process. |
| Outcome: | The proposed method produces high-quality generated data and significantly improves model performance. |
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
Copied to clipboard
| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
| Approach: | They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text. |
| Outcome: | The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts. |
Towards Generative Event Factuality Prediction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for event factuality prediction focus on author's presentation of factuity . a novel end-to-end generative task is proposed to predict event factuality holders, targets, and their associated factual values. |
| Approach: | They propose a generative task and system for predicting event factuality holders, targets, and their associated factual values. |
| Outcome: | The proposed system improves on the FactBank corpus and other corpora . it can predict presentation of factuality of nested sources alongside their target events . |
End-to-end Dense Video Captioning as Sequence Generation (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for dense video captioning use a two-stage generative process . but, more complex tasks are not able to fully utilize this powerful paradigm . |
| Approach: | They propose to model two subtasks of dense video captioning as one sequence generation task and predict the events and the corresponding descriptions. |
| Outcome: | Experiments on YouCook2 and ViTT show that the proposed model can be used on any video platform. |
Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to model event associations struggle with semantic ambiguity and embedding bias. |
| Approach: | They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model . |
| Outcome: | The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism. |
Integrating External Event Knowledge for Script Learning (2020.coling-main)
Copied to clipboard
| Challenge: | Recent studies focus on event co-occurrence to solve this problem. |
| Approach: | They propose to integrate external event knowledge to help predict the next event . they use a script-based approach that integrates external event information into the model . |
| Outcome: | The proposed method achieves state-of-the-art performance compared to other methods. |
Analogous Process Structure Induction for Sub-event Sequence Prediction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on event understanding is focusing on procedural (or horizontal) tasks such as predicting the next event given an observed sequence. |
| Approach: | They propose an Analogous Process Structure Induction framework which leverages analogies among processes and conceptualization of sub-event instances to predict the whole sub- sequence of previously unseen open-domain processes. |
| Outcome: | The proposed framework can predict the whole sub-event sequence of previously unseen open-domain processes. |