b'Sameer Singh'

research

∙ 07/08/2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues

Machine Learning (ML) models are increasingly used to make critical deci...

Dylan Slack, et al. ∙

research

∙ 05/25/2022

Learning to Query Internet Text for Informing Reinforcement Learning Agents

Generalization to out of distribution tasks in reinforcement learning is...

Kolby Nottingham, 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...

Sanjay Subramanian, 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...

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...

Yasaman Razeghi, et al. ∙

research

∙ 02/03/2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical...

Himabindu Lakkaraju, et al. ∙

research

∙ 01/21/2022

Identifying Adversarial Attacks on Text Classifiers

The landscape of adversarial attacks against text classifiers continues ...

Zhouhang Xie, et al. ∙

research

∙ 12/16/2021

FRUIT: Faithfully Reflecting Updated Information in Text

Textual knowledge bases such as Wikipedia require considerable effort to...

Robert L. Logan IV, et al. ∙

research

∙ 09/10/2021

Entity-Based Knowledge Conflicts in Question Answering

Knowledge-dependent tasks typically use two sources of knowledge: parame...

Shayne Longpre, et al. ∙

research

∙ 09/05/2021

Modular Framework for Visuomotor Language Grounding

Natural language instruction following tasks serve as a valuable test-be...

Kolby Nottingham, et al. ∙

research

∙ 07/13/2021

Enforcing Consistency in Weakly Supervised Semantic Parsing

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

Nitish Gupta, et al. ∙

research

∙ 07/01/2021

Combining Feature and Instance Attribution to Detect Artifacts

Training the large deep neural networks that dominate NLP requires large...

Pouya Pezeshkpour, et al. ∙

research

∙ 06/24/2021

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Prompting language models (LMs) with training examples and task descript...

Robert L. Logan IV, et al. ∙

research

∙ 06/23/2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated

As machine learning models are increasingly used in critical decision-ma...

Dylan Slack, et al. ∙

research

∙ 06/12/2021

Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP

Retrieval is a core component for open-domain NLP tasks. In open-domain ...

Anthony Chen, et al. ∙

research

∙ 06/04/2021

Counterfactual Explanations Can Be Manipulated

Counterfactual explanations are emerging as an attractive option for pro...

Dylan Slack, et al. ∙

research

∙ 04/18/2021

Generative Context Pair Selection for Multi-hop Question Answering

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

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...

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...

Matt Gardner, et al. ∙

research

∙ 04/09/2021

An Empirical Comparison of Instance Attribution Methods for NLP

Widespread adoption of deep models has motivated a pressing need for app...

Pouya Pezeshkpour, et al. ∙

research

∙ 04/05/2021

Paired Examples as Indirect Supervision in Latent Decision Models

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

Nitish Gupta, et al. ∙

research

∙ 02/19/2021

Calibrate Before Use: Improving Few-Shot Performance of Language Models

GPT-3 can perform numerous tasks when provided a natural language prompt...

Tony Z. Zhao, et al. ∙

research

∙ 10/29/2020

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

The remarkable success of pretrained language models has motivated the s...

Taylor Shin, et al. ∙

research

∙ 10/23/2020

Customizing Triggers with Concealed Data Poisoning

Adversarial attacks alter NLP model predictions by perturbing test-time ...

Eric Wallace, 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...

Sanjay Subramanian, et al. ∙

research

∙ 10/12/2020

Gradient-based Analysis of NLP Models is Manipulable

Gradient-based analysis methods, such as saliency map visualizations and...

Junlin Wang, 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...

Anthony Chen, et al. ∙

research

∙ 08/11/2020

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations

As local explanations of black box models are increasingly being employe...

Dylan Slack, et al. ∙

research

∙ 06/04/2020

Image Augmentations for GAN Training

Data augmentations have been widely studied to improve the accuracy and ...

Zhengli Zhao, et al. ∙

research

∙ 05/08/2020

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Although measuring held-out accuracy has been the primary approach to ev...

Marco Tulio Ribeiro, 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

∙ 04/06/2020

Evaluating NLP Models via Contrast Sets

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

Matt Gardner, et al. ∙

research

∙ 02/11/2020

Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Netw...

Zhengli Zhao, 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...

Dheeru Dua, et al. ∙

research

∙ 12/23/2019

Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

As deep reinforcement learning (RL) is applied to more tasks, there is a...

Piyush Gupta, et al. ∙

research

∙ 12/10/2019

Neural Module Networks for Reasoning over Text

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

Nitish Gupta, et al. ∙

research

∙ 11/06/2019

How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods

As machine learning black boxes are increasingly being deployed in domai...

Dylan Slack, et al. ∙

research

∙ 11/05/2019

Memory Augmented Recursive Neural Networks

Recursive neural networks have shown an impressive performance for model...

Forough Arabshahi, et al. ∙

research

∙ 10/02/2019

Improving Differentially Private Models with Active Learning

Broad adoption of machine learning techniques has increased privacy conc...

Zhengli Zhao, 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...

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...

Eric Wallace, et al. ∙

research

∙ 09/09/2019

Knowledge Enhanced Contextual Word Representations

Contextual word representations, typically trained on unstructured, unla...

Matthew E. Peters, et al. ∙

research

∙ 08/20/2019

Universal Adversarial Triggers for Attacking and Analyzing NLP

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

Eric Wallace, et al. ∙

research

∙ 08/20/2019

Universal Adversarial Triggers for NLP

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

Eric Wallace, et al. ∙

research

∙ 06/07/2019

Compositional Questions Do Not Necessitate Multi-hop Reasoning

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

Sewon Min, et al. ∙

research

∙ 05/02/2019

Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications

Representing entities and relations in an embedding space is a well-stud...

Pouya Pezeshkpour, et al. ∙

research

∙ 04/05/2019

PoMo: Generating Entity-Specific Post-Modifiers in Context

We introduce entity post-modifier generation as an instance of a collabo...

Jun Seok Kang, 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...

Dheeru Dua, et al. ∙

research

∙ 09/05/2018

Embedding Multimodal Relational Data for Knowledge Base Completion

Representing entities and relations in an embedding space is a well-stud...

Pouya Pezeshkpour, et al. ∙

research

∙ 08/28/2018

Interpretation of Natural Language Rules in Conversational Machine Reading

Most work in machine reading focuses on question answering problems wher...

Marzieh Saeidi, et al. ∙

Sameer Singh

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