Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering (2022.acl-long)
Copied to clipboard
| Challenge: | Existing work exploits easily accessible co-occurrence information of events to learn event representations. |
| Approach: | They propose a weakly supervised contrastive learning method and a prototype-based clustering method for event representation learning. |
| Outcome: | The proposed framework outperforms baselines on Hard Similarity and Transitive Sentence Similarity tasks. |
Similar Papers
Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for event extraction use annotated event types but are expensive and time-consuming. |
| Approach: | They propose a semi-supervised approach to learning new event types using a masked contrastive loss. |
| Outcome: | The proposed method learns similarities between clusters by enforcing an attention mechanism over the data minibatch. |
Event Representation Learning with Multi-Grained Contrastive Learning and Triple-Mixture of Experts (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for learning event representations fail to capture hidden feature information between events. |
| Approach: | They propose a method that extends the random masked language model by incorporating a specialized MLM to capture different grammatical structures within events. |
| Outcome: | The proposed method outperforms baselines in hard similarity and transitive sentence similarity tasks, highlighting the superiority of the proposed method. |
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (2022.naacl-main)
Copied to clipboard
| Challenge: | Identifying related entities and events within and across documents is fundamental to natural language understanding. |
| Approach: | They propose an approach to entity and event coreference resolution using contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art results on key metrics on the ECB+ corpus and is competitive on others. |
Joint Constrained Learning for Event-Event Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. |
| Approach: | They propose a joint constrained learning framework that enforces logical constraints within and across multiple temporal and subevent relations of events by converting constraints into differentiable learning objectives. |
| Outcome: | The proposed framework outperforms SOTA methods on benchmarks for temporal relation extraction and event hierarchy construction. |
Multi-Relational Probabilistic Event Representation Learning via Projected Gaussian Embedding (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for event representation learning ignore relations and uncertainty of events . Experimental results show that the proposed approach outperforms other state-of-the-art baselines on both existing and newly constructed datasets. |
| Approach: | They propose a novel approach to learning multi-relational probabilistic event embeddings based on contrastive learning. |
| Outcome: | The proposed method outperforms existing benchmarks on existing and newly constructed datasets. |
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (D19-1)
Copied to clipboard
| Challenge: | Existing systems treat this task as a pipeline of two separate subtasks, i.e., event extraction and temporal relation classification. |
| Approach: | They propose a joint event and temporal relation extraction model with shared representation learning and structured prediction. |
| Outcome: | The proposed method improves both event extraction and temporal relation extraction over state-of-the-art systems. |
Minimally Supervised Learning of Affective Events Using Discourse Relations (D19-1)
Copied to clipboard
| Challenge: | Existing methods for learning affective events that trigger positive or negative sentiment are difficult because of the unbounded combinatorial nature of language. |
| Approach: | They propose to propagate affective polarity using discourse relations using a small seed lexicon and large raw corpus. |
| Outcome: | The proposed method learns affective events effectively without manually labeled data, and improves supervised learning when labeles are small. |
Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for event extraction ignore motion representations in videos and are misguided by background noise. |
| Approach: | They propose a text-video based multimodal event extraction framework that integrates video appearance features and motion representations with video appearance. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in the event extraction field. |
Differentiable Data Augmentation for Contrastive Sentence Representation Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | a contrastive learning framework is used to fine-tune pre-trained language models with unlabeled sentences or labeled sentences. |
| Approach: | They propose a method that makes hard positives from unlabeled sentences . they use a prefix attached to a model to allow for differentiable data augmentation . |
| Outcome: | The proposed method yields significant improvements over existing methods under semi-supervised and supervised settings. |
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Structured knowledge representations capture temporal relations between events to describe human-level representations of common scenarios. |
| Approach: | They propose to represent narrative graphs and learn contextualized event representations over them using a relational graph neural network model. |
| Outcome: | The proposed model improves performance when learning script knowledge without supervision and provides a better representation for the implicit discourse sense classification task. |