Papers by Assaf Toledo
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
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Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy (2023.findings-eacl)
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Shai Gretz, Assaf Toledo, Roni Friedman, Dan Lahav, Rose Weeks, Naor Bar-Zeev, João Sedoc, Pooja Sangha, Yoav Katz, Noam Slonim
| Challenge: | As COVID-19 vaccines were rolled out, they were met with widespread hesitancy. |
| Approach: | They propose a new framework for intent discovery that leverages existing intent classifiers to provide a real-world conversational dataset of conversations conducted by actual users with VIRA. |
| Outcome: | The proposed framework enables users to find out what they are doing and why they are hesitant. |
More Bang for your Context: Virtual Documents for Question Answering over Long Documents (2024.findings-emnlp)
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| Challenge: | Large language models struggle to utilize long contexts efficiently, resulting in a question answering problem. |
| Approach: | They propose a method to generate a short document that contains the most relevant parts for a given context window. |
| Outcome: | The proposed method improves the QA task by providing a short and focused VDoc to the LLM while keeping the context window full. |
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. |