RevUp: Revise and Update Information Bottleneck for Event Representation (2023.eacl-main)
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| Challenge: | Existing external (“side”) semantic knowledge has been shown to result in more expressive computational event models. |
| Approach: | They propose a semi-supervised information bottleneck-based discrete latent variable model that reparameterizes discrete variables with auxiliary continuous latent variables and a light-weight hierarchical structure. |
| Outcome: | The proposed model outperforms existing models on multiple datasets. |
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| Challenge: | Existing methods for event modeling take discrete, external knowledge into account . obtaining fully accurate structured knowledge can be difficult . |
| Approach: | They propose a method that takes partially-observed sequences of discrete, external knowledge into account. |
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Cross-Modal Conceptualization in Bottleneck Models (2023.emnlp-main)
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Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov, Vladimir Ivanov, Alexey Kornaev, Ivan Titov
| Challenge: | Existing models that use text descriptions to predict labels are limited in their interpretations. |
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Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)
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| Challenge: | Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants. |
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EVEDIT: Event-based Knowledge Editing for Deterministic Knowledge Propagation (2024.emnlp-main)
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| Challenge: | Existing knowledge editing approaches only operate on (subject, relation, object) triple . current methods are limited to (substance, relation) triple, causing low confidence in their answers. |
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Cost-sensitive Regularization for Label Confusion-aware Event Detection (P19-1)
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| Challenge: | Recent advances in event detection focus on wordwise classification with one NIL class for tokens do not trigger any event. |
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A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (D19-1)
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| Challenge: | Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models. |
| Approach: | They propose a knowledge regularization approach that integrates linguistic constraints with contexts for deriving word representations. |
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Latent Concept-based Explanation of NLP Models (2024.emnlp-main)
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| Challenge: | Existing attempts to explain deep learning models rely on input features, such as the words . however, such explanations are often less informative due to the discrete nature of words and lack of contextual verbosity. |
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Utilizing Relative Event Time to Enhance Event-Event Temporal Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for event-event temporal relation extraction are sparse on event-time information. |
| Approach: | They propose a model for event-event temporal relation classification and an auxiliary task, relative event time prediction, which predicts the event time as real numbers. |
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Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)
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| Challenge: | Existing methods for identifying causal relations of events are limited . Existing approaches cannot handle well the problem, especially in the condition of lacking training data. |
| Approach: | They propose a Latent Structure Induction Network to integrate external structural knowledge into a causality reasoning task. |
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Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events (2020.emnlp-main)
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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. |
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