Hal Daumé III

research

∙ 05/13/2022

Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications

There are many ways to express similar things in text, which makes evalu...

Kaitlyn Zhou, et al. ∙

research

∙ 10/14/2021

Learning When and What to Ask: a Hierarchical Reinforcement Learning Framework

Reliable AI agents should be mindful of the limits of their knowledge an...

Khanh Nguyen, et al. ∙

research

∙ 10/10/2021

Distantly-Supervised Evidence Retrieval Enables Question Answering without Evidence Annotation

Open-domain question answering answers a question based on evidence retr...

Chen Zhao, et al. ∙

research

∙ 04/27/2021

A Human-Centered Interpretability Framework Based on Weight of Evidence

In this paper, we take a human-centered approach to interpretable machin...

David Alvarez-Melis, et al. ∙

research

∙ 04/13/2021

Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

Complex question answering often requires finding a reasoning chain that...

Chen Zhao, et al. ∙

research

∙ 11/30/2020

A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions

Peer review is the backbone of academia and humans constitute a cornerst...

Ivan Stelmakh, et al. ∙

research

∙ 11/30/2020

A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences

Conference peer review constitutes a human-computation process whose imp...

Ivan Stelmakh, et al. ∙

research

∙ 11/30/2020

Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review

Modern machine learning and computer science conferences are experiencin...

Ivan Stelmakh, et al. ∙

research

∙ 10/21/2020

On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries

Large-scale semantic parsing datasets annotated with logical forms have ...

Tianze Shi, et al. ∙

research

∙ 06/14/2020

Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms

We formulate the problem of learning to imitate multiple, non-determinis...

Khanh Nguyen, et al. ∙

research

∙ 05/28/2020

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their...

Su Lin Blodgett, et al. ∙

research

∙ 05/28/2020

Operationalizing the Legal Principle of Data Minimization for Personalization

Article 5(1)(c) of the European Union's General Data Protection Regulati...

Asia J. Biega, et al. ∙

research

∙ 05/26/2020

Active Imitation Learning with Noisy Guidance

Imitation learning algorithms provide state-of-the-art results on many s...

Kianté Brantley, et al. ∙

research

∙ 04/06/2020

Meta-Learning for Few-Shot NMT Adaptation

We present META-MT, a meta-learning approach to adapt Neural Machine Tra...

Amr Sharaf, et al. ∙

research

∙ 10/30/2019

Toward Gender-Inclusive Coreference Resolution

Correctly resolving textual mentions of people fundamentally entails mak...

Yang Trista Cao, et al. ∙

research

∙ 10/29/2019

Weight of Evidence as a Basis for Human-Oriented Explanations

Interpretability is an elusive but highly sought-after characteristic of...

David Alvarez-Melis, et al. ∙

research

∙ 10/01/2019

Global Voices: Crossing Borders in Automatic News Summarization

We construct Global Voices, a multilingual dataset for evaluating cross-...

Khanh Nguyen, et al. ∙

research

∙ 09/04/2019

Help, Anna! Visual Navigation with Natural Multimodal Assistance via Retrospective Curiosity-Encouraging Imitation Learning

Mobile agents that can leverage help from humans can potentially accompl...

Khanh Nguyen, et al. ∙

research

∙ 06/21/2019

Reinforcement Learning with Convex Constraints

In standard reinforcement learning (RL), a learning agent seeks to optim...

Sobhan Miryoosefi, et al. ∙

research

∙ 04/04/2019

Answer-based Adversarial Training for Generating Clarification Questions

We present an approach for generating clarification questions with the g...

Sudha Rao, et al. ∙

research

∙ 02/05/2019

Non-Monotonic Sequential Text Generation

Standard sequential generation methods assume a pre-specified generation...

Sean Welleck, et al. ∙

research

∙ 01/23/2019

Meta-Learning for Contextual Bandit Exploration

We describe MELEE, a meta-learning algorithm for learning a good explora...

Amr Sharaf, et al. ∙

research

∙ 01/02/2019

Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback

We investigate the feasibility of learning from both fully-labeled super...

Chicheng Zhang, et al. ∙

research

∙ 12/13/2018

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequi...

Kenneth Holstein, et al. ∙

research

∙ 10/29/2018

Content Selection in Deep Learning Models of Summarization

We carry out experiments with deep learning models of summarization acro...

Chris Kedzie, et al. ∙

research

∙ 07/17/2018

Contextual Memory Trees

We design and study a Contextual Memory Tree (CMT), a learning memory co...

Wen Sun, et al. ∙

research

∙ 05/12/2018

Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information

Inquiry is fundamental to communication, and machines cannot effectively...

Sudha Rao, et al. ∙

research

∙ 03/19/2018

When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks

Attacks against machine learning systems represent a growing threat as h...

Octavian Suciu, et al. ∙

research

∙ 03/01/2018

Hierarchical Imitation and Reinforcement Learning

We study the problem of learning policies over long time horizons. We pr...

Hoang M. Le, et al. ∙

research

∙ 11/04/2017

Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task

This paper presents a summary of the first Workshop on Building Linguist...

Allyson Ettinger, et al. ∙

research

∙ 08/03/2017

The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task

We describe the University of Maryland machine translation systems submi...

Amr Sharaf, et al. ∙

research

∙ 07/24/2017

Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Machine translation is a natural candidate problem for reinforcement lea...

Khanh Nguyen, et al. ∙

research

∙ 03/03/2017

Active Learning for Cost-Sensitive Classification

We design an active learning algorithm for cost-sensitive multiclass cla...

Akshay Krishnamurthy, et al. ∙

research

∙ 11/16/2016

The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives

Visual narrative is often a combination of explicit information and judi...

Mohit Iyyer, et al. ∙

research

∙ 06/15/2016

Logarithmic Time One-Against-Some

We create a new online reduction of multiclass classification to binary ...

Hal Daumé III, et al. ∙

research

∙ 11/30/2015

Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

The ability to comprehend wishes or desires and their fulfillment is imp...

Snigdha Chaturvedi, et al. ∙

research

∙ 11/30/2015

Modeling Dynamic Relationships Between Characters in Literary Novels

Studying characters plays a vital role in computationally representing a...

Snigdha Chaturvedi, et al. ∙

research

∙ 10/26/2015

Parser for Abstract Meaning Representation using Learning to Search

We develop a novel technique to parse English sentences into Abstract Me...

Sudha Rao, et al. ∙

research

∙ 03/18/2015

Learning to Search for Dependencies

We demonstrate that a dependency parser can be built using a credit assi...

Kai-Wei Chang, et al. ∙

research

∙ 02/08/2015

Learning to Search Better Than Your Teacher

Methods for learning to search for structured prediction typically imita...

Kai-Wei Chang, et al. ∙

research

∙ 08/09/2014

Bayesian Multitask Learning with Latent Hierarchies

We learn multiple hypotheses for related tasks under a latent hierarchic...

Hal Daumé III, et al. ∙

research

∙ 06/27/2012

Flexible Modeling of Latent Task Structures in Multitask Learning

Multitask learning algorithms are typically designed assuming some fixed...

Alexandre Passos, et al. ∙

research

∙ 06/27/2012

A Binary Classification Framework for Two-Stage Multiple Kernel Learning

With the advent of kernel methods, automating the task of specifying a s...

Abhishek Kumar, et al. ∙

research

∙ 06/27/2012

Learning Task Grouping and Overlap in Multi-task Learning

In the paradigm of multi-task learning, mul- tiple related prediction ta...

Abhishek Kumar, et al. ∙

research

∙ 04/16/2012

Efficient Protocols for Distributed Classification and Optimization

In distributed learning, the goal is to perform a learning task over dat...

Hal Daumé III, et al. ∙

research

∙ 02/27/2012

Protocols for Learning Classifiers on Distributed Data

We consider the problem of learning classifiers for labeled data that ha...

Hal Daumé III, et al. ∙

Hal Daumé III

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