| Challenge: | a new model of narrative schema knowledge does not capture the temporal relationships between events . a temporal order model is able to unscramble event sequences without access to labeled temporal training data . |
| Approach: | They propose a temporal order-based model that can be flexibly applied to different tasks . they use a BART-based conditional generation model that captures temporal co-occurrence . |
| Outcome: | The proposed model outperforms existing models on temporal ordering and event infilling tasks. |
Similar Papers
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. |
Conditional set generation using Seq2seq models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Several NLP tasks are instances of set generation. |
| Approach: | They propose a model-independent data augmentation approach that enlarges the model with the signals of order-invariance and cardinality. |
| Outcome: | The proposed method improves performance on four benchmark datasets with no additional annotations. |
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. |
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
Copied to clipboard
Miguel Ballesteros, Rishita Anubhai, Shuai Wang, Nima Pourdamghani, Yogarshi Vyas, Jie Ma, Parminder Bhatia, Kathleen McKeown, Yaser Al-Onaizan
| Challenge: | Existing models for temporal ordering of events rely on pretrained representations, transfer and multitask learning, and self-training techniques. |
| Approach: | They propose a neural architecture and a set of training methods for ordering events by predicting temporal relations by pre-training models. |
| Outcome: | The proposed models can predict temporal relations between two pairs of events within a span of text and identify temporal relationships between them. |
Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts (2022.naacl-main)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method generates more interesting stories with *flashbacks* while maintaining textual diversity, fluency, and temporal coherence. |
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models . |
| Approach: | They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties. |
| Outcome: | The proposed framework surpasses existing baselines for event temporal graph generation. |
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. |
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. |
Temporal Event Knowledge Acquisition via Identifying Narratives (P18-1)
Copied to clipboard
| Challenge: | Existing knowledge of narrative examples is lacking and difficult to obtain. |
| Approach: | They propose a weakly supervised approach for acquiring rich temporal event knowledge across sentences in narrative stories. |
| Outcome: | The proposed approach outperforms neural network models on the narrative cloze task. |
Diversity-aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction (D19-60)
Copied to clipboard
| Challenge: | Typical event sequences are important class of commonsense knowledge . previous work in event prediction uses sequence-to-sequence models . however, what can happen after a given event is usually diverse . |
| Approach: | They propose to incorporate a conditional variational autoencoder into seq2seq for its ability to represent diverse next events as a probabilistic distribution. |
| Outcome: | The proposed model outperforms deterministic models in terms of precision and recall . the proposed model is based on a conditional variational autoencoder . |