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20172025
most citedLanguage Generation with Recurrent Generative Adversarial Networks without Pre-training

90 citations · 442 across the 35 of their papers we have counts for

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Showing 2018Show all

13 papers · 1 filter

cs.CL2018

Value-based Search in Execution Space for Mapping Instructions to Programs

Dor Muhlgay, Jonathan Herzig, Jonathan Berant

Training models to map natural language instructions to programs given target world supervision only requires searching for good programs at training time. Search is commonly done…

cs.CL2018

CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Alon Talmor, Jonathan Herzig, Nicholas Lourie +1

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. Recent work has focused primarily on answering questions given s…

cs.CL2018

Evaluating Text GANs as Language Models

Guy Tevet, Gavriel Habib, Vered Shwartz +1

Generative Adversarial Networks (GANs) are a promising approach for text generation that, unlike traditional language models (LM), does not suffer from the problem of ``exposure bi…

cs.CL2018

Emergence of Communication in an Interactive World with Consistent Speakers

Ben Bogin, Mor Geva, Jonathan Berant

Training agents to communicate with one another given task-based supervision only has attracted considerable attention recently, due to the growing interest in developing models fo…

cs.CL2018

Explaining Queries over Web Tables to Non-Experts

Jonathan Berant, Daniel Deutch, Amir Globerson +2

Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) c…

cs.CL2018

Repartitioning of the ComplexWebQuestions Dataset

Alon Talmor, Jonathan Berant

Recently, Talmor and Berant (2018) introduced ComplexWebQuestions - a dataset focused on answering complex questions by decomposing them into a sequence of simpler questions and ex…