| Challenge: | Existing work induces mention representations independently by extracting features from the sentence that contains the mention, without using the context of the other mention. |
| Approach: | They propose a Pairwise Representation Learning scheme for the event mention pairs that jointly encodes a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. |
| Outcome: | The proposed scheme outperforms state-of-the-art representations on cross-document and within-document benchmarks. |
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| Challenge: | Recent work grouped granular events into more general events, called complex events . however, this approach assumes that a given complex event is always described in consecutive sentences . |
| Approach: | They propose a context-augmented representation learning approach that uses contextual information to model pairwise relation between granular events. |
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A Simple Unsupervised Approach for Coreference Resolution using Rule-based Weak Supervision (2022.starsem-1)
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| Challenge: | state-of-the-art coreference models rely on labeled data, but an end-to-end model is needed to solve this problem. |
| Approach: | They propose an approach that leverages an end-to-end neural model in settings where labeled data is unavailable. |
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Realistic Evaluation Principles for Cross-document Coreference Resolution (2021.starsem-1)
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| Challenge: | Using permissive evaluation protocols, cross-document coreference resolution models produce inflated results. |
| Approach: | They propose to decouple evaluation of mention detection from coreference linking . they argue that models should not exploit the synthetic topic structure of the standard ECB+ dataset . |
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JSEEGraph: Joint Structured Event Extraction as Graph Parsing (2023.starsem-1)
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| Challenge: | Existing approaches model event extraction using simplified datasets or sequence-labeling-based encodings. |
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Script Parsing with Hierarchical Sequence Modelling (2021.starsem-1)
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| Challenge: | Script knowledge is a category of commonsense knowledge that describes how people conduct everyday activities sequentially. |
| Approach: | They propose a hierarchical sequence model and transfer learning to do script parsing with a sequence model that accurately tags script participants. |
| Outcome: | The proposed model improves state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants. |
Event Semantic Knowledge in Procedural Text Understanding (2023.starsem-1)
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| Challenge: | Annotators’ reliance on commonsense knowledge to annotate implicit state information is a challenge for entity state tracking. |
| Approach: | They propose a method for entity state tracking that incorporates commonsense entity-centric knowledge from ConceptNet into a BERT-based neural-symbolic architecture. |
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Event Causality Identification via Generation of Important Context Words (2022.starsem-1)
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| Challenge: | Prior work focused on identifying causal relation between two event mentions . current models do not output important contexts for causal prediction of two mentions. |
| Approach: | They propose to use dependency path generation as a complementary task for ECI. |
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Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations (2022.starsem-1)
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| Challenge: | Existing models for online decoding on active input are not trained to handle an online decode environment. |
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Exploring Factual Entailment with NLI: A News Media Study (2024.starsem-1)
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| Challenge: | Recent studies have focused on the relationship between factuality and Natural Language Inference (NLI). |
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Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis (2021.starsem-1)
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| Challenge: | Compchains are a categorization of the hierarchy of predicate dependency relations present within a UD parse. |
| Approach: | They introduce compchains, a categorization of the hierarchy of predicate dependency relations present within a UD parse. |
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