Papers by Rotem Dror
Deep Dominance - How to Properly Compare Deep Neural Models (P19-1)
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| Challenge: | Existing methods for comparing DNNs on unseen data are not suitable for this task. |
| Approach: | They propose to adapt a test for the Almost Stochastic Dominance relation between two distributions to the problem by comparing their performance on unseen data. |
| Outcome: | The proposed method meets all criteria while previously proposed methods fail to do so. |
Human-in-the-loop Schema Induction (2023.acl-demo)
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Tianyi Zhang, Isaac Tham, Zhaoyi Hou, Jiaxuan Ren, Leon Zhou, Hainiu Xu, Li Zhang, Lara Martin, Rotem Dror, Sha Li, Heng Ji, Martha Palmer, Susan Windisch Brown, Reece Suchocki, Chris Callison-Burch
| Challenge: | Existing approaches to event-centric natural language understanding (NLU) have been limited to linear and temporal ones. |
| Approach: | They propose a human-in-the-loop schema induction system powered by GPT-3 . they show that it transfers to new domains more easily than previous approaches . |
| Outcome: | The proposed system transfers to new domains more easily than previous approaches and reduces human curation. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing (P18-1)
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| Challenge: | Statistical significance testing is a standard statistical tool designed to ensure that experimental results are not coincidental. |
| Approach: | They propose a protocol for statistical significance test selection in NLP setups . they propose he proposes a survey of the most relevant tests to help guide the protocol . |
| Outcome: | The proposed protocol includes a survey of the most relevant tests. |
On the Limitations of Reference-Free Evaluations of Generated Text (2022.emnlp-main)
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| Challenge: | a recent study has shown that evaluation metrics which accurately estimate the quality of generated text are limited in their ability to evaluate generated text. |
| Approach: | They argue that reference-free metrics are limited in their ability to evaluate generated text . they recommend that they be used as diagnostic tools for analyzing and understanding model behavior . |
| Outcome: | The proposed evaluation metrics are limited in their ability to evaluate generated text . they can be optimized at test time, can be biased against models with similar outputs . |
The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are widely used in NLP research but there is no standard or rigorous procedure to determine whether they can replace humans. |
| Approach: | They propose a statistical procedure that requires only a modest subset of annotated examples to justify using LLM annotations. |
| Outcome: | The proposed procedure compares LLMs with open-source LLM annotators and judges on ten language and vision-language tasks. |
A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods (2021.tacl-1)
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| Challenge: | Existing methods for summarization evaluations that approximate human judgments are lacking for accuracy and reliability. |
| Approach: | They propose methods for calculating confidence intervals and running hypothesis tests for correlations using bootstrapping and permutation. |
| Outcome: | The proposed methods show that the confidence intervals are wide, demonstrating high uncertainty in the reliability of automatic metrics. |
Zero-Shot On-the-Fly Event Schema Induction (2023.findings-eacl)
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| Challenge: | a new approach to event processing uses large language models to generate source documents that can be curated without manual data collection. |
| Approach: | They propose a framework that generates a graphical representation of events in documents . they show that the model is more complete than previous supervised methods . |
| Outcome: | The proposed model is more complete than human-curated schemas in most scenarios. |
Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics (2022.naacl-main)
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| Challenge: | Existing definitions of system-level correlations are inconsistent with how they are used to evaluate systems. |
| Approach: | They propose to calculate correlations only on pairs of systems separated by small differences in automatic scores . they propose to use the full test set instead of the subset of summaries judged by humans . |
| Outcome: | The proposed changes improve the accuracy of the estimated correlations on pairs of systems separated by small differences in automatic scores. |