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Found 15 papers, 3 papers with code
Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences
Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries.
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval
In this paper, we unify different multi-vector retrieval models from a token routing viewpoint and propose conditional token interaction via dynamic lexical routing, namely CITADEL, for efficient and effective multi-vector retrieval.
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Training with soft targets instead of hard targets has been shown to improve performance and calibration of deep neural networks.
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation
Natural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for NLP tasks such as Question Answering (QA) and Fact Verification.
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process.
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing
Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user's intents (set reminder, play music, etc.).
Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models
Discovering cause-effect relationships between variables from observational data is a fundamental challenge in many scientific disciplines.
Minimax bounds for structured prediction
Structured prediction can be considered as a generalization of many standard supervised learning tasks, and is usually thought as a simultaneous prediction of multiple labels.
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
In this paper, we propose a provably polynomial time randomized algorithm for learning the parameters of perturbed MAP predictors.
Learning linear structural equation models in polynomial time and sample complexity
We develop a new algorithm --- which is computationally and statistically efficient and works in the high-dimensional regime --- for learning linear SEMs from purely observational data with arbitrary noise distribution.
Learning Sparse Polymatrix Games in Polynomial Time and Sample Complexity
We also show that $\Omega(d \log (pm))$ samples are necessary for any method to consistently recover a game, with the same Nash-equilibria as the true game, from observations of strategic interactions.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
In this paper we propose a provably polynomial-time algorithm for learning sparse Gaussian Bayesian networks with equal noise variance --- a class of Bayesian networks for which the DAG structure can be uniquely identified from observational data --- under high-dimensional settings.
Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions
In this paper we obtain sufficient and necessary conditions on the number of samples required for exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions.
From Behavior to Sparse Graphical Games: Efficient Recovery of Equilibria
In this paper we study the problem of exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions of the players alone.
Information-theoretic limits of Bayesian network structure learning
In this paper, we study the information-theoretic limits of learning the structure of Bayesian networks (BNs), on discrete as well as continuous random variables, from a finite number of samples.
