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Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Real world applications such as economics and policy making often involv...
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Localized Calibration: Metrics and Recalibration
Probabilistic classifiers output confidence scores along with their pred...
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Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Modern machine learning models with high accuracy are often miscalibrate...
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Joint Energy-based Model Training for Better Calibrated Natural Language Understanding Models
In this work, we explore joint energy-based model (EBM) training during ...
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Robustness Gym: Unifying the NLP Evaluation Landscape
Despite impressive performance on standard benchmarks, deep neural netwo...
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FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging
Influence functions approximate the 'influences' of training data-points...
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Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
The early phase of training has been shown to be important in two ways f...
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Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
We present BRIDGE, a powerful sequential architecture for modeling depen...
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CTRLsum: Towards Generic Controllable Text Summarization
Current summarization systems yield generic summaries that are disconnec...
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GAEA: Graph Augmentation for Equitable Access via Reinforcement Learning
Disparate access to resources by different subpopulations is a prevalent...
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NaturalCC: A Toolkit to Naturalize the Source Code Corpus
We present NaturalCC, an efficient and extensible toolkit to bridge the ...
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Adapt-and-Adjust: Overcoming the Long-Tail Problem of Multilingual Speech Recognition
One crucial challenge of real-world multilingual speech recognition is t...
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CoMatch: Semi-supervised Learning with Contrastive Graph Regularization
Semi-supervised learning has been an effective paradigm for leveraging u...
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What's New? Summarizing Contributions in Scientific Literature
With thousands of academic articles shared on a daily basis, it has beco...
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Improving Limited Labeled Dialogue State Tracking with Self-Supervision
Existing dialogue state tracking (DST) models require plenty of labeled ...
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Probing Task-Oriented Dialogue Representation from Language Models
This paper investigates pre-trained language models to find out which mo...
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Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Intent detection is one of the core components of goal-oriented dialog s...
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Unsupervised Paraphrase Generation via Dynamic Blocking
We propose Dynamic Blocking, a decoding algorithm which enables large-sc...
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CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Dialogue state trackers have made significant progress on benchmark data...
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Online Structured Meta-learning
Learning quickly is of great importance for machine intelligence deploye...
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Explaining and Improving Model Behavior with k Nearest Neighbor Representations
Interpretability techniques in NLP have mainly focused on understanding ...
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How Important is the Train-Validation Split in Meta-Learning?
Meta-learning aims to perform fast adaptation on a new task through lear...
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Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning
It is not clear yet why ADAM-alike adaptive gradient algorithms suffer f...
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Representation Learning for Sequence Data with Deep Autoencoding Predictive Components
We propose Deep Autoencoding Predictive Components (DAPC) – a self-super...
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Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start
A standard way to address different NLP problems is by first constructin...
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Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Document interpretation and dialog understanding are the two major chall...
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GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
We present GraPPa, an effective pre-training approach for table semantic...
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Composed Variational Natural Language Generation for Few-shot Intents
In this paper, we focus on generating training examples for few-shot int...
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MoPro: Webly Supervised Learning with Momentum Prototypes
We propose a webly-supervised representation learning method that does n...
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Photon: A Robust Cross-Domain Text-to-SQL System
Natural language interfaces to databases (NLIDB) democratize end user ac...
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SummEval: Re-evaluating Summarization Evaluation
The scarcity of comprehensive up-to-date studies on evaluation metrics f...
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DART: Open-Domain Structured Data Record to Text Generation
We introduce DART, a large dataset for open-domain structured data recor...
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Theory-Inspired Path-Regularized Differential Network Architecture Search
Despite its high search efficiency, differential architecture search (DA...
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BERTology Meets Biology: Interpreting Attention in Protein Language Models
Transformer architectures have proven to learn useful representations fo...
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Towards Understanding Hierarchical Learning: Benefits of Neural Representations
Deep neural networks can empirically perform efficient hierarchical lear...
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A High-Quality Multilingual Dataset for Structured Documentation Translation
This paper presents a high-quality multilingual dataset for the document...
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WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos
Online action detection in untrimmed videos aims to identify an action a...
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EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
The goal of conversational machine reading is to answer user questions g...
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Prototypical Contrastive Learning of Unsupervised Representations
This paper presents Prototypical Contrastive Learning (PCL), an unsuperv...
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Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Word embeddings derived from human-generated corpora inherit strong gend...
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ESPRIT: Explaining Solutions to Physical Reasoning Tasks
Neural networks lack the ability to reason about qualitative physics and...
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VD-BERT: A Unified Vision and Dialog Transformer with BERT
Visual dialog is a challenging vision-language task, where a dialog agen...
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ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues
The use of pre-trained language models has emerged as a promising direct...
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Towards Noise-resistant Object Detection with Noisy Annotations
Training deep object detectors requires significant amount of human-anno...
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Differentially Private Deep Learning with Smooth Sensitivity
Ensuring the privacy of sensitive data used to train modern machine lear...
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Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
There is an increasing amount of literature that claims the brittleness ...
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Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
We introduce a parameterization method called Neural Bayes which allows ...
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Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width
We propose Taylorized training as an initiative towards better understan...
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Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills
Acquiring abilities in the absence of a task-oriented reward function is...
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Proposal Learning for Semi-Supervised Object Detection
In this paper, we focus on semi-supervised object detection to boost acc...
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