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20192022
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cs.CL2022

Multi-Domain Targeted Sentiment Analysis

Orith Toledo-Ronen, Matan Orbach, Yoav Katz +1

Targeted Sentiment Analysis (TSA) is a central task for generating insights from consumer reviews. Such content is extremely diverse, with sites like Amazon or Yelp containing revi…

cs.CL2020

YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews

Matan Orbach, Orith Toledo-Ronen, Artem Spector +3

Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets. Such an evaluation is limited, and may not reflect t…

cs.CL2020

Multilingual Argument Mining: Datasets and Analysis

Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu +2

The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets. However,…

cs.CL2020

Out of the Echo Chamber: Detecting Countering Debate Speeches

Matan Orbach, Yonatan Bilu, Assaf Toledo +4

An educated and informed consumption of media content has become a challenge in modern times. With the shift from traditional news outlets to social media and similar venues, a maj…

cs.CL2019

A Dataset of General-Purpose Rebuttal

Matan Orbach, Yonatan Bilu, Ariel Gera +8

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel tas…

cs.CL2019

Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining

Tamar Lavee, Matan Orbach, Lili Kotlerman +8

Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these argument…