b'Matt Gardner'

research

∙ 05/24/2023

Coverage-based Example Selection for In-Context Learning

In-context learning (ICL), the ability of large language models to perfo...

0 Shivanshu Gupta, et al. ∙

research

∙ 12/08/2022

Successive Prompting for Decomposing Complex Questions

Answering complex questions that require making latent decisions is a ch...

0 Dheeru Dua, et al. ∙

research

∙ 11/01/2022

CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation

The full power of human language-based communication cannot be realized ...

0 Abhilasha Ravichander, et al. ∙

research

∙ 05/17/2022

When to Use Multi-Task Learning vs Intermediate Fine-Tuning for Pre-Trained Encoder Transfer Learning

Transfer learning (TL) in natural language processing (NLP) has seen a s...

0 Orion Weller, et al. ∙

research

∙ 04/12/2022

ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension

Training a referring expression comprehension (ReC) model for a new visu...

4 Sanjay Subramanian, et al. ∙

research

∙ 03/24/2022

Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets

Natural language processing models often exploit spurious correlations b...

0 Yuxiang WU, et al. ∙

research

∙ 03/16/2022

Structurally Diverse Sampling Reduces Spurious Correlations in Semantic Parsing Datasets

A rapidly growing body of research has demonstrated the inability of NLP...

5 Shivanshu Gupta, et al. ∙

research

∙ 02/15/2022

Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Pretrained Language Models (LMs) have demonstrated ability to perform nu...

0 Yasaman Razeghi, et al. ∙

research

∙ 12/16/2021

Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks

Retrieval-augmented generation models have shown state-of-the-art perfor...

8 Akari Asai, 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...

1 Ben Bogin, et al. ∙

research

∙ 07/27/2021

QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension

Alongside huge volumes of research on deep learning models in NLP in the...

2 Anna Rogers, et al. ∙

research

∙ 07/15/2021

Tailor: Generating and Perturbing Text with Semantic Controls

Making controlled perturbations is essential for various tasks (e.g., da...

6 Alexis Ross, et al. ∙

research

∙ 07/13/2021

Enforcing Consistency in Weakly Supervised Semantic Parsing

The predominant challenge in weakly supervised semantic parsing is that ...

0 Nitish Gupta, et al. ∙

research

∙ 05/07/2021

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Readers of academic research papers often read with the goal of answerin...

21 Pradeep Dasigi, et al. ∙

research

∙ 04/18/2021

Documenting the English Colossal Clean Crawled Corpus

As language models are trained on ever more text, researchers are turnin...

7 Jesse Dodge, et al. ∙

research

∙ 04/18/2021

Generative Context Pair Selection for Multi-hop Question Answering

Compositional reasoning tasks like multi-hop question answering, require...

5 Dheeru Dua, et al. ∙

research

∙ 04/18/2021

Learning with Instance Bundles for Reading Comprehension

When training most modern reading comprehension models, all the question...

13 Dheeru Dua, et al. ∙

research

∙ 04/17/2021

Competency Problems: On Finding and Removing Artifacts in Language Data

Much recent work in NLP has documented dataset artifacts, bias, and spur...

0 Matt Gardner, et al. ∙

research

∙ 04/05/2021

Paired Examples as Indirect Supervision in Latent Decision Models

Compositional, structured models are appealing because they explicitly d...

0 Nitish Gupta, et al. ∙

research

∙ 03/22/2021

Mitigating False-Negative Contexts in Multi-document QuestionAnswering with Retrieval Marginalization

Question Answering (QA) tasks requiring information from multiple docume...

0 Ansong Ni, et al. ∙

research

∙ 11/16/2020

Learning from Task Descriptions

Typically, machine learning systems solve new tasks by training on thous...

0 Orion Weller, et al. ∙

research

∙ 11/13/2020

IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

Humans often have to read multiple documents to address their informatio...

0 James Ferguson, et al. ∙

research

∙ 10/12/2020

MedICaT: A Dataset of Medical Images, Captions, and Textual References

Understanding the relationship between figures and text is key to scient...

18 Sanjay Subramanian, et al. ∙

research

∙ 10/12/2020

Improving Compositional Generalization in Semantic Parsing

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

0 Inbar Oren, et al. ∙

research

∙ 10/07/2020

MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics

Posing reading comprehension as a generation problem provides a great de...

0 Anthony Chen, et al. ∙

research

∙ 10/06/2020

Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq

High-quality and large-scale data are key to success for AI systems. How...

0 Qiang Ning, et al. ∙

research

∙ 09/20/2020

Understanding Mention Detector-Linker Interaction for Neural Coreference Resolution

Coreference resolution is an important task for discourse-level natural ...

0 Zhaofeng Wu, 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...

7 Ben Bogin, et al. ∙

research

∙ 05/02/2020

Obtaining Faithful Interpretations from Compositional Neural Networks

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

22 Sanjay Subramanian, et al. ∙

research

∙ 05/01/2020

TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

A critical part of reading is being able to understand the temporal rela...

0 Qiang Ning, et al. ∙

research

∙ 04/06/2020

Multi-Step Inference for Reasoning Over Paragraphs

Complex reasoning over text requires understanding and chaining together...

0 Jiangming Liu, et al. ∙

research

∙ 04/06/2020

Evaluating NLP Models via Contrast Sets

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

0 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...

0 Tomer Wolfson, et al. ∙

research

∙ 12/29/2019

ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Reading comprehension is one of the crucial tasks for furthering researc...

0 Dheeru Dua, et al. ∙

research

∙ 12/10/2019

Neural Module Networks for Reasoning over Text

Answering compositional questions that require multiple steps of reasoni...

0 Nitish Gupta, 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 ...

0 Matt Gardner, et al. ∙

research

∙ 09/19/2019

AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Neural NLP models are increasingly accurate but are imperfect and opaque...

0 Eric Wallace, et al. ∙

research

∙ 09/17/2019

Do NLP Models Know Numbers? Probing Numeracy in Embeddings

The ability to understand and work with numbers (numeracy) is critical f...

0 Eric Wallace, et al. ∙

research

∙ 09/08/2019

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

We introduce the first open-domain dataset, called QuaRTz, for reasoning...

0 Oyvind Tafjord, 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...

0 Ben Bogin, et al. ∙

research

∙ 08/20/2019

Universal Adversarial Triggers for Attacking and Analyzing NLP

Adversarial examples highlight model vulnerabilities and are useful for ...

0 Eric Wallace, et al. ∙

research

∙ 08/20/2019

Universal Adversarial Triggers for NLP

Adversarial examples highlight model vulnerabilities and are useful for ...

0 Eric Wallace, et al. ∙

research

∙ 08/16/2019

Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability...

0 Kevin Lin, et al. ∙

research

∙ 08/16/2019

Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning

Machine comprehension of texts longer than a single sentence often requi...

0 Pradeep Dasigi, et al. ∙

research

∙ 06/07/2019

Compositional Questions Do Not Necessitate Multi-hop Reasoning

Multi-hop reading comprehension (RC) questions are challenging because t...

6 Sewon Min, 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 ...

0 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...

0 Ben Bogin, et al. ∙

research

∙ 03/21/2019

Linguistic Knowledge and Transferability of Contextual Representations

Contextual word representations derived from large-scale neural language...

0 Nelson F. Liu, et al. ∙

research

∙ 03/01/2019

DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reading comprehension has recently seen rapid progress, with systems mat...

0 Dheeru Dua, et al. ∙

research

∙ 11/20/2018

QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships

Many natural language questions require recognizing and reasoning with q...

0 Oyvind Tafjord, et al. ∙

Matt Gardner

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