206 citations · 240 across the 17 of their papers we have counts for
20 papers
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Eyal Shnarch, Alon Halfon, Ariel Gera +19
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML kn…
Quality Controlled Paraphrase Generation
Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer +3
Paraphrase generation has been widely used in various downstream tasks. Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet…
Cluster & Tune: Boost Cold Start Performance in Text Classification
Eyal Shnarch, Ariel Gera, Alon Halfon +4
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained model…
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis
Liat Ein-Dor, Ilya Shnayderman, Artem Spector +3
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks. However, in many cases, the…
TWEETSUMM -- A Dialog Summarization Dataset for Customer Service
Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder +3
In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the…
Overview of the 2021 Key Point Analysis Shared Task
Roni Friedman, Lena Dankin, Yufang Hou +3
We describe the 2021 Key Point Analysis (KPA-2021) shared task on key point analysis that we organized as a part of the 8th Workshop on Argument Mining (ArgMining 2021) at EMNLP 20…
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…
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation
Ishan Jindal, Ranit Aharonov, Siddhartha Brahma +2
Deep neural models achieve some of the best results for semantic role labeling. Inspired by instance-based learning that utilizes nearest neighbors to handle low-frequency context-…
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains
Eyal Shnarch, Leshem Choshen, Guy Moshkowich +2
Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of…
A Survey of the State of Explainable AI for Natural Language Processing
Marina Danilevsky, Kun Qian, Ranit Aharonov +3
Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an…
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…
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…
Corpus Wide Argument Mining -- a Working Solution
Liat Ein-Dor, Eyal Shnarch, Lena Dankin +10
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively sm…
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.…
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…
Controversy in Context
Benjamin Sznajder, Ariel Gera, Yonatan Bilu +5
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior w…
Fast End-to-End Wikification
Ilya Shnayderman, Liat Ein-Dor, Yosi Mass +7
Wikification of large corpora is beneficial for various NLP applications. Existing methods focus on quality performance rather than run-time, and are therefore non-feasible for lar…
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…
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
Martin Gleize, Eyal Shnarch, Leshem Choshen +4
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding an…
Learning Concept Abstractness Using Weak Supervision
Ella Rabinovich, Benjamin Sznajder, Artem Spector +4
We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data. Exploiting only minimal lingu…