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A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions
Peer review is the backbone of academia and humans constitute a cornerst...
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A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences
Conference peer review constitutes a human-computation process whose imp...
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Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review
Modern machine learning and computer science conferences are experiencin...
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On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries
Large-scale semantic parsing datasets annotated with logical forms have ...
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Active Imitation Learning from Multiple Non-Deterministic Teachers: Formulation, Challenges, and Algorithms
We formulate the problem of learning to imitate multiple, non-determinis...
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Language (Technology) is Power: A Critical Survey of "Bias" in NLP
We survey 146 papers analyzing "bias" in NLP systems, finding that their...
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Operationalizing the Legal Principle of Data Minimization for Personalization
Article 5(1)(c) of the European Union's General Data Protection Regulati...
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Active Imitation Learning with Noisy Guidance
Imitation learning algorithms provide state-of-the-art results on many s...
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Meta-Learning for Few-Shot NMT Adaptation
We present META-MT, a meta-learning approach to adapt Neural Machine Tra...
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Toward Gender-Inclusive Coreference Resolution
Correctly resolving textual mentions of people fundamentally entails mak...
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Weight of Evidence as a Basis for Human-Oriented Explanations
Interpretability is an elusive but highly sought-after characteristic of...
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Global Voices: Crossing Borders in Automatic News Summarization
We construct Global Voices, a multilingual dataset for evaluating cross-...
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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...
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Reinforcement Learning with Convex Constraints
In standard reinforcement learning (RL), a learning agent seeks to optim...
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Answer-based Adversarial Training for Generating Clarification Questions
We present an approach for generating clarification questions with the g...
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Non-Monotonic Sequential Text Generation
Standard sequential generation methods assume a pre-specified generation...
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Meta-Learning for Contextual Bandit Exploration
We describe MELEE, a meta-learning algorithm for learning a good explora...
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Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
We investigate the feasibility of learning from both fully-labeled super...
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Improving fairness in machine learning systems: What do industry practitioners need?
The potential for machine learning (ML) systems to amplify social inequi...
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Content Selection in Deep Learning Models of Summarization
We carry out experiments with deep learning models of summarization acro...
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Contextual Memory Trees
We design and study a Contextual Memory Tree (CMT), a learning memory co...
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Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information
Inquiry is fundamental to communication, and machines cannot effectively...
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When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks
Attacks against machine learning systems represent a growing threat as h...
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Hierarchical Imitation and Reinforcement Learning
We study the problem of learning policies over long time horizons. We pr...
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Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
This paper presents a summary of the first Workshop on Building Linguist...
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The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
We describe the University of Maryland machine translation systems submi...
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Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Machine translation is a natural candidate problem for reinforcement lea...
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Active Learning for Cost-Sensitive Classification
We design an active learning algorithm for cost-sensitive multiclass cla...
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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...
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Logarithmic Time One-Against-Some
We create a new online reduction of multiclass classification to binary ...
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Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
The ability to comprehend wishes or desires and their fulfillment is imp...
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Modeling Dynamic Relationships Between Characters in Literary Novels
Studying characters plays a vital role in computationally representing a...
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Parser for Abstract Meaning Representation using Learning to Search
We develop a novel technique to parse English sentences into Abstract Me...
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Learning to Search for Dependencies
We demonstrate that a dependency parser can be built using a credit assi...
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Learning to Search Better Than Your Teacher
Methods for learning to search for structured prediction typically imita...
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Bayesian Multitask Learning with Latent Hierarchies
We learn multiple hypotheses for related tasks under a latent hierarchic...
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Flexible Modeling of Latent Task Structures in Multitask Learning
Multitask learning algorithms are typically designed assuming some fixed...
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A Binary Classification Framework for Two-Stage Multiple Kernel Learning
With the advent of kernel methods, automating the task of specifying a s...
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Learning Task Grouping and Overlap in Multi-task Learning
In the paradigm of multi-task learning, mul- tiple related prediction ta...
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Efficient Protocols for Distributed Classification and Optimization
In distributed learning, the goal is to perform a learning task over dat...
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Protocols for Learning Classifiers on Distributed Data
We consider the problem of learning classifiers for labeled data that ha...
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