activity
20192022
most citedAutomatic Argument Quality Assessment -- New Datasets and Methods

6 citations · 10 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CL2022★ 1 cited

VIRATrustData: A Trust-Annotated Corpus of Human-Chatbot Conversations About COVID-19 Vaccines

Roni Friedman, João Sedoc, Shai Gretz +5

Public trust in medical information is crucial for successful application of public health policies such as vaccine uptake. This is especially true when the information is offered…

cs.CL2022★ 2 cited

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

Shai Gretz, Assaf Toledo, Roni Friedman +7

The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address th…

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★ 1 cited

A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis

Shai Gretz, Roni Friedman, Edo Cohen-Karlik +4

Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging…

cs.CL2019★ 6 cited

Automatic Argument Quality Assessment -- New Datasets and Methods

Assaf Toledo, Shai Gretz, Edo Cohen-Karlik +6

We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data.…

cs.CL2019

Learning to combine Grammatical Error Corrections

Yoav Kantor, Yoav Katz, Leshem Choshen +5

The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-bo…

cs.CL2019

Syntactic Interchangeability in Word Embedding Models

Daniel Hershcovich, Assaf Toledo, Alon Halfon +1

Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which wor…