Papers with SemEval 2018 Task
What do Entity-Centric Models Learn? Insights from Entity Linking in Multi-Party Dialogue (N19-1)
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| Challenge: | a recent study suggests that models that incorporate a bias towards learning entity representations are not effective at modeling entities. |
| Approach: | They propose to use two entity-centric models for a referential task . they show they outperform the state of the art and do better on lower frequency entities . |
| Outcome: | The proposed models outperform the state of the art on a referential task . they do better on lower frequency entities than a counterpart model not entity-centric . |
Tackling Irony Detection using Ensemble Classifiers (2022.lrec-1)
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| Challenge: | Automated approaches to irony detection still fall short of what one would consider desirable performance. |
| Approach: | They propose to use transformer-based approaches to automate irony detection in social media . they propose to augmentation training data to address the binary and fine-grained problem . |
| Outcome: | The proposed methods improve performance over baselines and are not decisive for good results. |