Papers by Matan Orbach
Welcome to the Real World: Efficient, Incremental and Scalable Key Point Analysis (2023.emnlp-industry)
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| Challenge: | Key Point Analysis (KPA) extracts the main points from opinions and quantifies their prevalence. |
| Approach: | They propose a key point analysis framework that extracts the main points from opinions and quantifies their prevalence. |
| Outcome: | The proposed system is able to match sentences to key points over five datasets and demonstrate its performance. |
A Dataset of General-Purpose Rebuttal (D19-1)
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Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | a key element in argumentation is rebuttal, the ability to contest an argument by presenting a counter-argument. |
| Approach: | They propose a method based on general rebuttal arguments to produce a critical response to a long argumentative text. |
| Outcome: | The proposed method overcomes the need for topic-specific arguments to be provided . it allows creating responses beyond the scope of topics for which specific arguments are available . |
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI (2024.naacl-demo)
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Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman, Ofir Arviv, Matan Orbach, Shachar Don-Yehiya, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, Yoav Katz
| Challenge: | Textual data processing pipelines are tailored to specific datasets, task and model combinations. |
| Approach: | They propose a library for customizable textual data preparation and evaluation tailored to generative language models. |
| Outcome: | Unitxt is a library for customizable textual data preparation and evaluation tailored to generative language models. |
Listening Comprehension over Argumentative Content (D18-1)
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Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
| Challenge: | In argumentation domain, people are exposed directly to audio (or the video), without access to a written version. |
| Approach: | They present a task for machine listening comprehension in the argumentation domain and a dataset in English. |
| Outcome: | The proposed task is based on 200 speeches arguing for or against 50 controversial topics and uses baseline methods to address it. |
Advances in Debating Technologies: Building AI That Can Debate Humans (2021.acl-tutorials)
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| Challenge: | This tutorial focuses on Debating Technologies, a sub-field of computational argumentation defined as "computational technologies developed directly to enhance, support, and engage with human debating" the tutorial provides a holistic view of a debated system, and discusses practical applications and future challenges of debation technologies. |
| Approach: | They present a tutorial on Debating Technologies, a sub-field of computational argumentation . they introduce Project Debater, which is the first AI system to debate human experts . |
| Outcome: | The project Debater is the first AI system to debate human experts on complex topics. |
Multilingual Argument Mining: Datasets and Analysis (2020.findings-emnlp)
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| Challenge: | Argument mining tasks in non-English languages are dominated by English . we use a pre-trained language model that supports 104 languages to train models . |
| Approach: | They propose a multilingual BERT model to address argument mining tasks in non-English languages . they use English datasets and machine translation to facilitate transfer learning . |
| Outcome: | The proposed model is well suited for classifying the stance of arguments and detecting evidence, but less so for assessing the quality of arguments. |
Multi-Domain Targeted Sentiment Analysis (2022.naacl-main)
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| Challenge: | Targeted Sentiment Analysis (TSA) is a task for generating insights from consumer reviews. |
| Approach: | They propose a multi-domain TSA system that augments a given training set with diverse weak labels from assorted domains and augments it with Yelp reviews. |
| Outcome: | The proposed model outperforms manual methods on three evaluation datasets across different domains and shows that it performs well. |
Crowd-sourcing annotation of complex NLU tasks: A case study of argumentative content annotation (D19-59)
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| Challenge: | Recent advances in machine reading and listening comprehension involve the annotation of long texts. |
| Approach: | They propose a way to perform a sentence-by-sentence annotation task with crowd annotators. |
| Outcome: | The proposed approach can be used to identify claims in a debate speech. |
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews (2021.emnlp-main)
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| Challenge: | YASO contains 2,215 English sentences from dozens of review domains, annotated with target terms and their sentiment. |
| Approach: | They propose a new TSA evaluation dataset of open-domain user reviews in English . YASO contains 2,215 English sentences annotated with target terms and their sentiment . |
| Outcome: | The proposed dataset verifies the reliability of the annotations and explores the characteristics of the collected data. |
Out of the Echo Chamber: Detecting Countering Debate Speeches (2020.acl-main)
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| Challenge: | Existing algorithms to detect articles that counter the arguments in debate speeches are unsuccessful, suggesting room for further research. |
| Approach: | They propose a task to detect articles that counter the arguments made in debate speeches by annotating them from a dataset of 3,685 such speeches. |
| Outcome: | The proposed algorithm can detect articles that counter the arguments made in debate speeches, and some are successful, but none are human-like. |