Jonathan Berant

research

∙ 05/25/2022

QAMPARI: : An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs

Existing benchmarks for open-domain question answering (ODQA) typically ...

Samuel Joseph Amouyal, et al. ∙

research

∙ 04/28/2022

Inferring Implicit Relations with Language Models

A prominent challenge for modern language understanding systems is the a...

Uri Katz, et al. ∙

research

∙ 02/13/2022

Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments

Neural scaling laws define a predictable relationship between a model's ...

Maor Ivgi, et al. ∙

research

∙ 01/15/2022

Unobserved Local Structures Make Compositional Generalization Hard

While recent work has convincingly showed that sequence-to-sequence mode...

Ben Bogin, et al. ∙

research

∙ 01/10/2022

SCROLLS: Standardized CompaRison Over Long Language Sequences

NLP benchmarks have largely focused on short texts, such as sentences an...

Uri Shaham, et al. ∙

research

∙ 12/16/2021

Learning To Retrieve Prompts for In-Context Learning

In-context learning is a recent paradigm in natural language understandi...

Ohad Rubin, et al. ∙

research

∙ 12/14/2021

Learning to Retrieve Passages without Supervision

Dense retrievers for open-domain question answering (ODQA) have been sho...

Ori Ram, et al. ∙

research

∙ 12/12/2021

Weakly Supervised Mapping of Natural Language to SQL through Question Decomposition

Natural Language Interfaces to Databases (NLIDBs), where users pose quer...

Tomer Wolfson, et al. ∙

research

∙ 09/22/2021

COVR: A test-bed for Visually Grounded Compositional Generalization with real images

While interest in models that generalize at test time to new composition...

Ben Bogin, et al. ∙

research

∙ 09/06/2021

Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization

Modern semantic parsers suffer from two principal limitations. First, tr...

Inbar Oren, et al. ∙

research

∙ 07/29/2021

Break, Perturb, Build: Automatic Perturbation of Reasoning Paths through Question Decomposition

Recent efforts to create challenge benchmarks that test the abilities of...

Mor Geva, et al. ∙

research

∙ 07/15/2021

Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills

Models pre-trained with a language modeling objective possess ample worl...

Ori Yoran, et al. ∙

research

∙ 06/13/2021

Memory-efficient Transformers via Top-k Attention

Following the success of dot-product attention in Transformers, numerous...

Ankit Gupta, et al. ∙

research

∙ 04/17/2021

Question Decomposition with Dependency Graphs

QDMR is a meaning representation for complex questions, which decomposes...

Matan Hasson, et al. ∙

research

∙ 04/13/2021

What's in your Head? Emergent Behaviour in Multi-Task Transformer Models

The primary paradigm for multi-task training in natural language process...

Mor Geva, et al. ∙

research

∙ 04/11/2021

Achieving Model Robustness through Discrete Adversarial Training

Discrete adversarial attacks are symbolic perturbations to a language in...

Maor Ivgi, et al. ∙

research

∙ 03/17/2021

Value-aware Approximate Attention

Following the success of dot-product attention in Transformers, numerous...

Ankit Gupta, et al. ∙

research

∙ 03/09/2021

BERTese: Learning to Speak to BERT

Large pre-trained language models have been shown to encode large amount...

Adi Haviv, et al. ∙

research

∙ 01/06/2021

Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

A key limitation in current datasets for multi-hop reasoning is that the...

Mor Geva, et al. ∙

research

∙ 01/02/2021

Few-Shot Question Answering by Pretraining Span Selection

In a number of question answering (QA) benchmarks, pretrained models hav...

Ori Ram, et al. ∙

research

∙ 12/29/2020

Transformer Feed-Forward Layers Are Key-Value Memories

Feed-forward layers constitute two-thirds of a transformer model's param...

Mor Geva, et al. ∙

research

∙ 10/23/2020

SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

The de-facto standard decoding method for semantic parsing in recent yea...

Ohad Rubin, et al. ∙

research

∙ 10/12/2020

Improving Compositional Generalization in Semantic Parsing

Generalization of models to out-of-distribution (OOD) data has captured ...

Inbar Oren, et al. ∙

research

∙ 09/30/2020

Learning Object Detection from Captions via Textual Scene Attributes

Object detection is a fundamental task in computer vision, requiring lar...

Achiya Jerbi, et al. ∙

research

∙ 09/23/2020

Scene Graph to Image Generation with Contextualized Object Layout Refinement

Generating high-quality images from scene graphs, that is, graphs that d...

Maor Ivgi, et al. ∙

research

∙ 09/13/2020

Span-based Semantic Parsing for Compositional Generalization

Despite the success of sequence-to-sequence (seq2seq) models in semantic...

Jonathan Herzig, et al. ∙

research

∙ 09/02/2020

A Simple Global Neural Discourse Parser

Discourse parsing is largely dominated by greedy parsers with manually-d...

Yichu Zhou, et al. ∙

research

∙ 07/01/2020

Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering

Answering questions that involve multi-step reasoning requires decomposi...

Ben Bogin, et al. ∙

research

∙ 06/11/2020

Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

To what extent can a neural network systematically reason over symbolic ...

Alon Talmor, et al. ∙

research

∙ 06/05/2020

GMAT: Global Memory Augmentation for Transformers

Transformer-based models have become ubiquitous in natural language proc...

Jonathan Berant, et al. ∙

research

∙ 05/02/2020

Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...

Sanjay Subramanian, et al. ∙

research

∙ 04/12/2020

Explaining Question Answering Models through Text Generation

Large pre-trained language models (LMs) have been shown to perform surpr...

Veronica Latcinnik, et al. ∙

research

∙ 04/09/2020

Injecting Numerical Reasoning Skills into Language Models

Large pre-trained language models (LMs) are known to encode substantial ...

Mor Geva, et al. ∙

research

∙ 04/06/2020

Evaluating the Evaluation of Diversity in Natural Language Generation

Despite growing interest in natural language generation (NLG) models tha...

Guy Tevet, et al. ∙

research

∙ 04/06/2020

Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...

Matt Gardner, et al. ∙

research

∙ 01/31/2020

Break It Down: A Question Understanding Benchmark

Understanding natural language questions entails the ability to break do...

Tomer Wolfson, et al. ∙

research

∙ 12/31/2019

oLMpics – On what Language Model Pre-training Captures

Recent success of pre-trained language models (LMs) has spurred widespre...

Alon Talmor, et al. ∙

research

∙ 10/05/2019

On the Limits of Learning to Actively Learn Semantic Representations

One of the goals of natural language understanding is to develop models ...

Omri Koshorek, et al. ∙

research

∙ 09/25/2019

Question Answering is a Format; When is it Useful?

Recent years have seen a dramatic expansion of tasks and datasets posed ...

Matt Gardner, et al. ∙

research

∙ 08/29/2019

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

State-of-the-art semantic parsers rely on auto-regressive decoding, emit...

Ben Bogin, et al. ∙

research

∙ 08/26/2019

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

A major hurdle on the road to conversational interfaces is the difficult...

Jonathan Herzig, et al. ∙

research

∙ 08/21/2019

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...

Mor Geva, et al. ∙

research

∙ 05/31/2019

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

A large number of reading comprehension (RC) datasets has been created r...

Alon Talmor, et al. ∙

research

∙ 05/30/2019

Grammar-based Neural Text-to-SQL Generation

The sequence-to-sequence paradigm employed by neural text-to-SQL models ...

Kevin Lin, et al. ∙

research

∙ 05/15/2019

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

Research on parsing language to SQL has largely ignored the structure of...

Ben Bogin, et al. ∙

research

∙ 04/04/2019

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

Adversarial examples are important for understanding the behavior of neu...

Yotam Gil, et al. ∙

research

∙ 02/27/2019

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

Sentence fusion is the task of joining several independent sentences int...

Mor Geva, et al. ∙

research

∙ 02/26/2019

Learning Latent Scene-Graph Representations for Referring Relationships

Understanding the semantics of complex visual scenes often requires anal...

Moshiko Raboh, et al. ∙

research

∙ 01/13/2019

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

Deep neural networks work well at approximating complicated functions wh...

Alon Jacovi, et al. ∙

research

∙ 11/02/2018

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

Training models to map natural language instructions to programs given t...

Dor Muhlgay, et al. ∙

Jonathan Berant

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