Summary of Major Entity Identification: a Generalizable Alternative to Coreference Resolution, by Kawshik Manikantan et al.
Major Entity Identification: A Generalizable Alternative to Coreference Resolution
by Kawshik Manikantan, Shubham Toshniwal, Makarand Tapaswi, Vineet Gandhi
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper aims to address the limited generalization of coreference resolution (CR) models by introducing an alternative referential task called Major Entity Identification (MEI). MEI assumes the target entities are specified in the input and focuses on identifying frequent entities. The authors demonstrate that MEI models generalize well across domains using supervised models and LLM-based few-shot prompting, outperforming traditional CR approaches. The paper also introduces a classification framework for MEI, enabling the use of robust metrics. This approach has practical applications, allowing users to search for mentions of specific entities or groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Coreference resolution is a task that’s hard for machines to do well. Currently, we can’t easily use these models in new situations because they don’t generalize well. One reason for this is the way we label our training data. Instead of trying to fix the annotation problems, researchers have proposed using extra labeled data from new domains. This paper suggests a different approach called Major Entity Identification (MEI). MEI assumes you know which important entities are in the text and tries to find all mentions of those entities. The authors tested this idea and found it works well across many datasets. They also showed how to measure MEI’s performance using easy-to-understand metrics. This could be useful for people who want to search for specific information. |
Keywords
» Artificial intelligence » Classification » Coreference » Few shot » Generalization » Prompting » Supervised
