Summary of Easyecr: a Library For Easy Implementation and Evaluation Of Event Coreference Resolution Models, by Yuncong Li et al.
EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models
by Yuncong Li, Tianhua Xu, Sheng-hua Zhong, Haiqin Yang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses two key challenges in Event Coreference Resolution (ECR): limited generalizability across domains due to narrow dataset evaluations and difficulties in comparing models within diverse ECR pipelines. To tackle these issues, the authors develop EasyECR, an open-source library that standardizes data structures and abstracts ECR pipelines for easy implementation and fair evaluation. The library integrates seven representative pipelines and ten popular benchmark datasets, enabling model evaluations on various datasets and promoting the development of robust ECR pipelines. Extensive evaluation via EasyECR reveals that representative ECR pipelines cannot generalize across multiple datasets, highlighting the need to evaluate pipelines on multiple datasets. The study also emphasizes the importance of ensuring consistency in models when comparing pipeline performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier for researchers to work with Event Coreference Resolution (ECR). Right now, it’s hard to compare different ECR methods because they use different data and approaches. To fix this, the authors created a special library called EasyECR that makes it easy to use and evaluate different ECR methods. This library includes many popular datasets and ECR methods, so researchers can test their own methods on these datasets and compare them to others. The results show that different ECR methods don’t work well across all datasets, which means researchers need to test their methods on multiple datasets. Overall, this paper helps make it easier for researchers to develop and compare ECR methods. |
Keywords
» Artificial intelligence » Coreference
