| Challenge: | Existing script knowledge models only represent a single event relationship, co-occurrence . this is coarse for commonsense, which should account for fine-grained relationships . |
| Approach: | They propose to view learning event embedding as a multi-relational problem . they model a rich set of event relations derived from the Penn Discourse Tree Bank . |
| Outcome: | The proposed model captures different aspects of event pairs, including cause and contrast. |
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| Challenge: | Existing methods focus on graph triples with event overlap, but ignore more supportive triples . Script reasoning relies on understanding the relationship between two events . |
| Approach: | They propose a model to learn the inferential relations between events from the whole eventuality KG . they propose 'script adapter' to extend the model to infer the associated relations between an event chain and a subsequent event candidate. |
| Outcome: | The proposed model is compared with baselines using external KG or not on a script reasoning task. |
Multi-Relational Probabilistic Event Representation Learning via Projected Gaussian Embedding (2023.findings-acl)
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| 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. |
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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. |
| 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. |
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Multi-Label Classification for Implicit Discourse Relation Recognition (2024.findings-acl)
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| Challenge: | Prior research in discourse relation recognition has treated these instances as separate examples during training, with a gold-standard prediction matching one of the labels considered correct at test time. |
| Approach: | They propose to use multiple labels to annotate an example when multiple relations are believed to hold simultaneously. |
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LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients (2025.emnlp-industry)
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Egor Fadeev, Dzhambulat Mollaev, Aleksei Shestov, Dima Korolev, Omar Zoloev, Ivan A Kireev, Andrey Savchenko, Maksim Makarenko
| Challenge: | Large language models (LLMs) are computationally expensive and impractical for real-world pipelines. |
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Heterogeneous Graph Neural Networks to Predict What Happen Next (2020.coling-main)
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| Challenge: | Existing work on event representation cannot capture discontinuous event segments . Existing models cannot represent heterogeneous relations and discontinuous events . |
| Approach: | They propose a heterogeneous-event graph network to model missing events . they employ each unique word and individual event as nodes in the graph . |
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Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)
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| 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 . |
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Building Context-aware Clause Representations for Situation Entity Type Classification (D18-1)
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| Challenge: | Existing models for categorizing clauses based on situation entity types do not provide accurate results. |
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From Discourse to Narrative: Knowledge Projection for Event Relation Extraction (2021.acl-long)
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| 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 simple but effective model for attachment in discourse parsing with multi-task learning for relation labeling (2023.eacl-main)
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| Challenge: | a discourse parsing model for conversation trained on the STAC is hard due to the complexity of discourse graphs and the frequent lack of surface cues provided by EDUs. |
| Approach: | They propose a discourse parsing model for conversation trained on the STAC that encodes discourse units and uses a multitask setting to predict relation labels. |
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