Jonathan Berant

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Tel Aviv University

Senior Lecturer (Assistant Professor) at Tel-Aviv University

Span-based Semantic Parsing for Compositional Generalization

Despite the success of sequence-to-sequence (seq2seq) models in semantic...
09/13/2020 ∙ by Jonathan Herzig, et al. ∙ 0 ∙ share

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A Simple Global Neural Discourse Parser

Discourse parsing is largely dominated by greedy parsers with manually-d...
09/02/2020 ∙ by Yichu Zhou, et al. ∙ 0 ∙ share

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Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering

Answering questions that involve multi-step reasoning requires decomposi...
07/01/2020 ∙ by Ben Bogin, et al. ∙ 7 ∙ share

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Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

To what extent can a neural network systematically reason over symbolic ...
06/11/2020 ∙ by Alon Talmor, et al. ∙ 0 ∙ share

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GMAT: Global Memory Augmentation for Transformers

Transformer-based models have become ubiquitous in natural language proc...
06/05/2020 ∙ by Jonathan Berant, et al. ∙ 8 ∙ share

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Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...
05/02/2020 ∙ by Sanjay Subramanian, et al. ∙ 22 ∙ share

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Explaining Question Answering Models through Text Generation

Large pre-trained language models (LMs) have been shown to perform surpr...
04/12/2020 ∙ by Veronica Latcinnik, et al. ∙ 24 ∙ share

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Injecting Numerical Reasoning Skills into Language Models

Large pre-trained language models (LMs) are known to encode substantial ...
04/09/2020 ∙ by Mor Geva, et al. ∙ 0 ∙ share

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Evaluating the Evaluation of Diversity in Natural Language Generation

Despite growing interest in natural language generation (NLG) models tha...
04/06/2020 ∙ by Guy Tevet, et al. ∙ 0 ∙ share

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Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...
04/06/2020 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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Break It Down: A Question Understanding Benchmark

Understanding natural language questions entails the ability to break do...
01/31/2020 ∙ by Tomer Wolfson, et al. ∙ 0 ∙ share

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oLMpics – On what Language Model Pre-training Captures

Recent success of pre-trained language models (LMs) has spurred widespre...
12/31/2019 ∙ by Alon Talmor, et al. ∙ 14 ∙ share

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On the Limits of Learning to Actively Learn Semantic Representations

One of the goals of natural language understanding is to develop models ...
10/05/2019 ∙ by Omri Koshorek, et al. ∙ 0 ∙ share

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Question Answering is a Format; When is it Useful?

Recent years have seen a dramatic expansion of tasks and datasets posed ...
09/25/2019 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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Global Reasoning over Database Structures for Text-to-SQL Parsing

State-of-the-art semantic parsers rely on auto-regressive decoding, emit...
08/29/2019 ∙ by Ben Bogin, et al. ∙ 0 ∙ share

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Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing

A major hurdle on the road to conversational interfaces is the difficult...
08/26/2019 ∙ by Jonathan Herzig, et al. ∙ 20 ∙ share

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Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets

Crowdsourcing has been the prevalent paradigm for creating natural langu...
08/21/2019 ∙ by Mor Geva, et al. ∙ 0 ∙ share

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MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension

A large number of reading comprehension (RC) datasets has been created r...
05/31/2019 ∙ by Alon Talmor, et al. ∙ 7 ∙ share

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Grammar-based Neural Text-to-SQL Generation

The sequence-to-sequence paradigm employed by neural text-to-SQL models ...
05/30/2019 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

Research on parsing language to SQL has largely ignored the structure of...
05/15/2019 ∙ by Ben Bogin, et al. ∙ 0 ∙ share

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White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks

Adversarial examples are important for understanding the behavior of neu...
04/04/2019 ∙ by Yotam Gil, et al. ∙ 6 ∙ share

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DiscoFuse: A Large-Scale Dataset for Discourse-based Sentence Fusion

Sentence fusion is the task of joining several independent sentences int...
02/27/2019 ∙ by Mor Geva, et al. ∙ 0 ∙ share

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Learning Latent Scene-Graph Representations for Referring Relationships

Understanding the semantics of complex visual scenes often requires anal...
02/26/2019 ∙ by Moshiko Raboh, et al. ∙ 6 ∙ share

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Neural network gradient-based learning of black-box function interfaces

Deep neural networks work well at approximating complicated functions wh...
01/13/2019 ∙ by Alon Jacovi, et al. ∙ 14 ∙ share

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

Training models to map natural language instructions to programs given t...
11/02/2018 ∙ by Dor Muhlgay, et al. ∙ 0 ∙ share

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CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

When answering a question, people often draw upon their rich world knowl...
11/02/2018 ∙ by Alon Talmor, et al. ∙ 0 ∙ share

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Evaluating Text GANs as Language Models

Generative Adversarial Networks (GANs) are a promising approach for text...
10/30/2018 ∙ by Guy Tevet, et al. ∙ 0 ∙ share

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Emergence of Communication in an Interactive World with Consistent Speakers

Training agents to communicate with one another given task-based supervi...
09/03/2018 ∙ by Ben Bogin, et al. ∙ 0 ∙ share

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Explaining Queries over Web Tables to Non-Experts

Designing a reliable natural language (NL) interface for querying tables...
08/14/2018 ∙ by Jonathan Berant, et al. ∙ 4 ∙ share

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Repartitioning of the ComplexWebQuestions Dataset

Recently, Talmor and Berant (2018) introduced ComplexWebQuestions - a da...
07/25/2018 ∙ by Alon Talmor, et al. ∙ 0 ∙ share

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Memory Augmented Policy Optimization for Program Synthesis with Generalization

This paper presents Memory Augmented Policy Optimization (MAPO): a novel...
07/06/2018 ∙ by Chen Liang, et al. ∙ 6 ∙ share

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Learning to Search in Long Documents Using Document Structure

Reading comprehension models are based on recurrent neural networks that...
06/09/2018 ∙ by Mor Geva, et al. ∙ 0 ∙ share

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Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing

Building a semantic parser quickly in a new domain is a fundamental chal...
04/21/2018 ∙ by Jonathan Herzig, et al. ∙ 0 ∙ share

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Text Segmentation as a Supervised Learning Task

Text segmentation, the task of dividing a document into contiguous segme...
03/25/2018 ∙ by Omri Koshorek, et al. ∙ 0 ∙ share

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Polyglot Semantic Parsing in APIs

Traditional approaches to semantic parsing (SP) work by training individ...
03/19/2018 ∙ by Kyle Richardson, et al. ∙ 0 ∙ share

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The Web as a Knowledge-base for Answering Complex Questions

Answering complex questions is a time-consuming activity for humans that...
03/18/2018 ∙ by Alon Talmor, et al. ∙ 0 ∙ share

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Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction

Structured prediction is concerned with predicting multiple inter-depend...
02/15/2018 ∙ by Roei Herzig, et al. ∙ 0 ∙ share

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Contextualized Word Representations for Reading Comprehension

Reading a document and extracting an answer to a question about its cont...
12/10/2017 ∙ by Shimi Salant, et al. ∙ 0 ∙ share

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Weakly-supervised Semantic Parsing with Abstract Examples

Semantic parsers translate language utterances to programs, but are ofte...
11/14/2017 ∙ by Omer Goldman, et al. ∙ 0 ∙ share

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Inducing Regular Grammars Using Recurrent Neural Networks

Grammar induction is the task of learning a grammar from a set of exampl...
10/28/2017 ∙ by Mor Cohen, et al. ∙ 0 ∙ share

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Evaluating Semantic Parsing against a Simple Web-based Question Answering Model

Semantic parsing shines at analyzing complex natural language that invol...
07/14/2017 ∙ by Alon Talmor, et al. ∙ 0 ∙ share

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Language Generation with Recurrent Generative Adversarial Networks without Pre-training

Generative Adversarial Networks (GANs) have shown great promise recently...
06/05/2017 ∙ by Ofir Press, et al. ∙ 0 ∙ share

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Neural Semantic Parsing over Multiple Knowledge-bases

A fundamental challenge in developing semantic parsers is the paucity of...
02/06/2017 ∙ by Jonathan Herzig, et al. ∙ 0 ∙ share

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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)

Extending the success of deep neural networks to natural language unders...
12/04/2016 ∙ by Chen Liang, et al. ∙ 0 ∙ share

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Hierarchical Question Answering for Long Documents

We present a framework for question answering that can efficiently scale...
11/06/2016 ∙ by Eunsol Choi, et al. ∙ 0 ∙ share

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Learning Recurrent Span Representations for Extractive Question Answering

The reading comprehension task, that asks questions about a given eviden...
11/04/2016 ∙ by Kenton Lee, et al. ∙ 0 ∙ share

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Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

Harnessing the statistical power of neural networks to perform language ...
10/31/2016 ∙ by Chen Liang, et al. ∙ 0 ∙ share

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Learning to generalize to new compositions in image understanding

Recurrent neural networks have recently been used for learning to descri...
08/27/2016 ∙ by Yuval Atzmon, et al. ∙ 0 ∙ share

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Jonathan Berant

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