no code implementations • COLING 2014 • Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund
no code implementations • EACL 2017 • Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, Noam Slonim
Recent work has addressed the problem of detecting relevant claims for a given controversial topic.
no code implementations • EMNLP 2017 • Eyal Shnarch, Ran Levy, Vikas Raykar, Noam Slonim
A human observer may notice the following underlying common structure, or pattern: [someone][argue/suggest/state][that][topic term][sentiment term].
no code implementations • WS 2017 • Ran Levy, Shai Gretz, Benjamin Sznajder, Shay Hummel, Ranit Aharonov, Noam Slonim
Automatic claim detection is a fundamental argument mining task that aims to automatically mine claims regarding a topic of consideration.
no code implementations • WS 2017 • Roy Bar-Haim, Lilach Edelstein, Charles Jochim, Noam Slonim
Stance classification is a core component in on-demand argument construction pipelines.
no code implementations • LREC 2018 • Shachar Mirkin, Michal Jacovi, Tamar Lavee, Hong-Kwang Kuo, Samuel Thomas, Leslie Sager, Lili Kotlerman, Elad Venezian, Noam Slonim
This paper describes an English audio and textual dataset of debating speeches, a unique resource for the growing research field of computational argumentation and debating technologies.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 23 Jan 2018 • Yosi Mass, Lili Kotlerman, Shachar Mirkin, Elad Venezian, Gera Witzling, Noam Slonim
We describe a large, high-quality benchmark for the evaluation of Mention Detection tools.
no code implementations • ACL 2018 • Liat Ein Dor, Yosi Mass, Alon Halfon, Elad Venezian, Ilya Shnayderman, Ranit Aharonov, Noam Slonim
We train a triplet network to embed sentences from the same section closer.
no code implementations • ACL 2018 • Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim
We propose a methodology to blend high quality but scarce strong labeled data with noisy but abundant weak labeled data during the training of neural networks.
no code implementations • COLING 2018 • Ran Levy, Ben Bogin, Shai Gretz, Ranit Aharonov, Noam Slonim
Our results clearly indicate that the system is able to successfully generalize from the weak signal, outperforming previously reported results in terms of both precision and coverage.
no code implementations • COLING 2018 • Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, Noam Slonim
Sentiment composition is a fundamental sentiment analysis problem.
no code implementations • EMNLP 2018 • Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
We introduce a weakly supervised approach for inferring the property of abstractness of words and expressions in the complete absence of labeled data.
no code implementations • EMNLP 2018 • Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
We applied baseline methods addressing the task, to be used as a benchmark for future work over this dataset.
Automatic Speech Recognition (ASR) Machine Reading Comprehension +1
1 code implementation • WS 2019 • Daniel Hershcovich, Assaf Toledo, Alon Halfon, Noam Slonim
Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly.
1 code implementation • WS 2019 • Yoav Kantor, Yoav Katz, Leshem Choshen, Edo Cohen-Karlik, Naftali Liberman, Assaf Toledo, Amir Menczel, Noam Slonim
We also present a spellchecker created for this task which outperforms standard spellcheckers tested on the task of spellchecking.
Ranked #8 on Grammatical Error Correction on BEA-2019 (test)
no code implementations • ACL 2019 • Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim
When debating a controversial topic, it is often desirable to expand the boundaries of discussion.
no code implementations • ACL 2019 • Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments.
no code implementations • WS 2019 • Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Shachar Mirkin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim
To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech.
no code implementations • 19 Aug 2019 • Ilya Shnayderman, Liat Ein-Dor, Yosi Mass, Alon Halfon, Benjamin Sznajder, Artem Spector, Yoav Katz, Dafna Sheinwald, Ranit Aharonov, Noam Slonim
Wikification of large corpora is beneficial for various NLP applications.
no code implementations • 20 Aug 2019 • Benjamin Sznajder, Ariel Gera, Yonatan Bilu, Dafna Sheinwald, Ella Rabinovich, Ranit Aharonov, David Konopnicki, Noam Slonim
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is.
no code implementations • ACL 2019 • Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron, Noam Slonim
In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.
no code implementations • IJCNLP 2019 • Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
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.
no code implementations • 3 Sep 2019 • Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
no code implementations • IJCNLP 2019 • Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results.
no code implementations • WS 2019 • Tamar Lavee, Lili Kotlerman, Matan Orbach, Yonatan Bilu, Michal Jacovi, Ranit Aharonov, Noam Slonim
Recent advancements in machine reading and listening comprehension involve the annotation of long texts.
no code implementations • 25 Nov 2019 • Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic.
no code implementations • WS 2019 • Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques.
2 code implementations • 26 Nov 2019 • Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim
To this end, we created a corpus of 30, 497 arguments carefully annotated for point-wise quality, released as part of this work.
no code implementations • ACL 2020 • Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
An educated and informed consumption of media content has become a challenge in modern times.
no code implementations • ACL 2020 • Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim
Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem.
no code implementations • 17 Sep 2020 • Yonatan Bilu, Shai Gretz, Edo Cohen, Noam Slonim
One of the most impressive human endeavors of the past two decades is the collection and categorization of human knowledge in the free and accessible format that is Wikipedia.
2 code implementations • EMNLP 2020 • Roy Bar-Haim, Yoav Kantor, Lilach Eden, Roni Friedman, Dan Lahav, Noam Slonim
Recent work has proposed to summarize arguments by mapping them to a small set of expert-generated key points, where the salience of each key point corresponds to the number of its matching arguments.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Shai Gretz, Yonatan Bilu, Edo Cohen-Karlik, Noam Slonim
Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Orith Toledo-Ronen, Matan Orbach, Yonatan Bilu, Artem Spector, Noam Slonim
The growing interest in argument mining and computational argumentation brings with it a plethora of Natural Language Understanding (NLU) tasks and corresponding datasets.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Noam Slonim, Ranit Aharonov
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 domain adaptation methods is unsatisfactory.
2 code implementations • EMNLP 2021 • Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, Noam Slonim
Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets.
Ranked #1 on Aspect Extraction on YASO - YELP
no code implementations • 1 Jan 2021 • Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim
In such low resources scenarios, we suggest performing an unsupervised classification task prior to fine-tuning on the target task.
no code implementations • ACL 2021 • Roy Bar-Haim, Lilach Eden, Yoav Kantor, Roni Friedman, Noam Slonim
Previous work on review summarization focused on measuring the sentiment toward the main aspects of the reviewed product or business, or on creating a textual summary.
no code implementations • ACL 2021 • Roy Bar-Haim, Liat Ein-Dor, Matan Orbach, Elad Venezian, Noam Slonim
We present a complete pipeline of a debating system, and discuss the information flow and the interaction between the various components.
no code implementations • EMNLP (ACL) 2021 • Roy Bar-Haim, Yoav Kantor, Elad Venezian, Yoav Katz, Noam Slonim
Engaging in a live debate requires a diverse set of skills, and Project Debater has been developed accordingly as a collection of components, each designed to perform a specific subtask.
no code implementations • EMNLP (ArgMining) 2021 • Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim
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 2021.
1 code implementation • 6 Jan 2022 • Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim
In recent years, pretrained language models have revolutionized the NLP world, while achieving state of the art performance in various downstream tasks.
1 code implementation • ACL 2022 • Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce.
1 code implementation • ACL 2022 • Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor
Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.
no code implementations • 29 Mar 2022 • Benjamin Sznajder, Chulaka Gunasekara, Guy Lev, Sachin Joshi, Eyal Shnarch, Noam Slonim
We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries.
2 code implementations • 6 Apr 2022 • Leshem Choshen, Elad Venezian, Noam Slonim, Yoav Katz
We also show that fusing is often better than intertraining.
no code implementations • NAACL 2022 • Orith Toledo-Ronen, Matan Orbach, Yoav Katz, Noam Slonim
Our results and analysis show that our approach is a promising step towards a practical domain-robust TSA system.
no code implementations • 22 May 2022 • Yotam Perlitz, Liat Ein-Dor, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer
Generating natural language statements to convey logical inferences from tabular data (i. e., Logical NLG) is a process with one input and a variety of valid outputs.
no code implementations • 24 May 2022 • Roni Friedman, João Sedoc, Shai Gretz, Assaf Toledo, Rose Weeks, Naor Bar-Zeev, Yoav Katz, Noam Slonim
Public trust in medical information is crucial for successful application of public health policies such as vaccine uptake.
1 code implementation • 24 May 2022 • Shai Gretz, Assaf Toledo, Roni Friedman, Dan Lahav, Rose Weeks, Naor Bar-Zeev, João Sedoc, Pooja Sangha, Yoav Katz, Noam Slonim
We use this framework to report baseline intent discovery results over VIRADialogs, that highlight the difficulty of this task.
1 code implementation • 2 Aug 2022 • Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martin Santillan Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz
Text classification can be useful in many real-world scenarios, saving a lot of time for end users.
no code implementations • 31 Aug 2022 • Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qingqing Cao, Manuel R. Ciosici, Michael Hassid, Kenneth Heafield, Sara Hooker, Colin Raffel, Pedro H. Martins, André F. T. Martins, Jessica Zosa Forde, Peter Milder, Edwin Simpson, Noam Slonim, Jesse Dodge, Emma Strubell, Niranjan Balasubramanian, Leon Derczynski, Iryna Gurevych, Roy Schwartz
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.
no code implementations • 31 Oct 2022 • Leshem Choshen, Elad Venezian, Shachar Don-Yehia, Noam Slonim, Yoav Katz
Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset.
1 code implementation • 31 Oct 2022 • Ariel Gera, Alon Halfon, Eyal Shnarch, Yotam Perlitz, Liat Ein-Dor, Noam Slonim
Recent advances in large pretrained language models have increased attention to zero-shot text classification.
no code implementations • 8 Nov 2022 • Yotam Perlitz, Dafna Sheinwald, Noam Slonim, Michal Shmueli-Scheuer
We present nBIIG, a neural Business Intelligence (BI) Insights Generation system.
no code implementations • 2 Dec 2022 • Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen
We propose a new paradigm to continually evolve pretrained models, denoted ColD Fusion.
no code implementations • 20 Dec 2022 • Elron Bandel, Yoav Katz, Noam Slonim, Liat Ein-Dor
We offer our protocol as a simple yet strong baseline for works that wish to make incremental advancements in the field of attribute controlled text rewriting.
no code implementations • 9 Feb 2023 • Almog Gueta, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, Leshem Choshen
Notably, we show that language models that have been finetuned on the same dataset form a tight cluster in the weight space, while models finetuned on different datasets from the same underlying task form a looser cluster.
1 code implementation • 2 May 2023 • Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.
no code implementations • 24 May 2023 • Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer, Dafna Sheinwald, Noam Slonim, Liat Ein-Dor
In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model.
no code implementations • 22 Aug 2023 • Yotam Perlitz, Elron Bandel, Ariel Gera, Ofir Arviv, Liat Ein-Dor, Eyal Shnarch, Noam Slonim, Michal Shmueli-Scheuer, Leshem Choshen
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities.
1 code implementation • EMNLP 2020 • Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim
Here, we present a large-scale empirical study on active learning techniques for BERT-based classification, addressing a diverse set of AL strategies and datasets.