Search Results for author: Michal Shmueli-Scheuer

Found 19 papers, 5 papers with code

Overview of the First Workshop on Scholarly Document Processing (SDP)

no code implementations EMNLP (sdp) 2020 Muthu Kumar Chandrasekaran, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard

To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event.

Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI

1 code implementation25 Jan 2024 Elron Bandel, Yotam Perlitz, Elad Venezian, Roni Friedman-Melamed, Ofir Arviv, Matan Orbach, Shachar Don-Yehyia, Dafna Sheinwald, Ariel Gera, Leshem Choshen, Michal Shmueli-Scheuer, Yoav Katz

In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, and model combinations.

Efficient Benchmarking of Language Models

no code implementations22 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.

Benchmarking

Active Learning for Natural Language Generation

no code implementations24 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.

Active Learning text-classification +2

Diversity Enhanced Table-to-Text Generation via Type Control

no code implementations22 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.

Table-to-Text Generation valid +1

Quality Controlled Paraphrase Generation

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.

Paraphrase Generation Sentence

HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles

1 code implementation7 Oct 2021 Odellia Boni, Guy Feigenblat, Guy Lev, Michal Shmueli-Scheuer, Benjamin Sznajder, David Konopnicki

We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources.

Abstractive Text Summarization Document Summarization +1

orgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions

no code implementations3 Sep 2020 Guy Lev, Michal Shmueli-Scheuer, Achiya Jerbi, David Konopnicki

Thus, we release orgFAQ, a new dataset composed of $6988$ user questions and $1579$ corresponding FAQs that were extracted from organizations' FAQ webpages in the Jobs domain.

Bot2Vec: Learning Representations of Chatbots

no code implementations SEMEVAL 2019 Jonathan Herzig, S, Tommy bank, Michal Shmueli-Scheuer, David Konopnicki

Chatbots (i. e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms.

Detecting Egregious Conversations between Customers and Virtual Agents

no code implementations NAACL 2018 Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski

In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction.

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