Papers by Yannis Katsis
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours (2022.emnlp-demos)
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Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang
| Challenge: | Label Sleuth is an open source system for labeling and creating text classifiers which does not require coding skills nor machine learning knowledge. |
| Approach: | *Label Sleuth* is an open source system for labeling and creating text classifiers which does not require coding skills nor machine learning knowledge. |
| Outcome: | *Label Sleuth* is an open source system for labeling and creating text classifiers. |
AIT-QA: Question Answering Dataset over Complex Tables in the Airline Industry (2022.naacl-industry)
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Yannis Katsis, Saneem Chemmengath, Vishwajeet Kumar, Samarth Bharadwaj, Mustafa Canim, Michael Glass, Alfio Gliozzo, Feifei Pan, Jaydeep Sen, Karthik Sankaranarayanan, Soumen Chakrabarti
| Challenge: | Table Question Answering (Table QA) systems have been shown to be highly accurate when trained and tested on open-domain datasets built on top of Wikipedia tables. |
| Approach: | They propose a domain-specific Table QA test dataset to test Table Question Answering systems on open-domain datasets built on top of Wikipedia tables. |
| Outcome: | The proposed methods are highly accurate when tested on open-domain datasets built on top of Wikipedia tables. |
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture (2023.findings-emnlp)
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Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, Dakuo Wang
| Challenge: | Existing low-resource learning techniques focus on label annotation while neglecting the natural language explanation of a data point. |
| Approach: | They propose a novel architecture that leverages an explanation-generation model to produce explanations guided by human explanations and a prediction model that utilizes generated explanations toward prediction faithfully. |
| Outcome: | The proposed architecture produces explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a data diversity-based AL sampling strategy that benefits from the explanation annotations. |
Zero-shot Topical Text Classification with LLMs - an Experimental Study (2023.findings-emnlp)
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Shai Gretz, Alon Halfon, Ilya Shnayderman, Orith Toledo-Ronen, Artem Spector, Lena Dankin, Yannis Katsis, Ofir Arviv, Yoav Katz, Noam Slonim, Liat Ein-Dor
| Challenge: | Topical text classification is an ancient, yet timely research area in natural language processing. |
| Approach: | They compare the zero-shot performance of a variety of LMs over a large dataset of 23 publicly available TTC datasets. |
| Outcome: | The proposed models outperform their counterparts over a large dataset and show that they perform better in a zero-shot scenario. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |
Development of an Enterprise-Grade Contract Understanding System (2021.naacl-industry)
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Arvind Agarwal, Laura Chiticariu, Poornima Chozhiyath Raman, Marina Danilevsky, Diman Ghazi, Ankush Gupta, Shanmukha Guttula, Yannis Katsis, Rajasekar Krishnamurthy, Yunyao Li, Shubham Mudgal, Vitobha Munigala, Nicholas Phan, Dhaval Sonawane, Sneha Srinivasan, Sudarshan R. Thitte, Mitesh Vasa, Ramiya Venkatachalam, Vinitha Yaski, Huaiyu Zhu
| Challenge: | Currently, legal contract review remains an expensive and arduous process. |
| Approach: | They describe a commercial system designed and deployed for contract understanding that enables legal professionals to review contracts. |
| Outcome: | The proposed system is used by a wide range of enterprise users and solves three major challenges. |
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations (2026.findings-acl)
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| Challenge: | Several benchmarks have been released to evaluate model performance on multi-turn retrieval augment generation tasks. |
| Approach: | They propose to benchmark 666 conversations with over 2,800 conversation turns across 6 domains and a corpora that focuses on unanswerable questions and later conversation turns. |
| Outcome: | The proposed benchmarks show that retrieval and generation models struggle on conversations with UNanswerable, UNderspecified, and NONstandalone questions and UNclear responses. |
Identifying Noise in Human-Created Datasets using Training Dynamics from Generative Models (2025.findings-emnlp)
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| Challenge: | Existing noise detection techniques for autoencoder models do not generalize to ArLMs due to differences in learning dynamics. |
| Approach: | They propose a method that leverages training dynamics to rank datapoints from easy-to-learn to hard-tolear . TDRanker achieves at least 2x faster denoising than previous techniques . |
| Outcome: | The proposed method demonstrates robustness across multiple model architectures and noise levels. |