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LIBLINEAR: A Library for Large Linear Classification
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-line tools and library…
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
TLDR
This work empirically demonstrates that its algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks.
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VisualBERT: A Simple and Performant Baseline for Vision and Language
TLDR
Analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments.
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A dual coordinate descent method for large-scale linear SVM
TLDR
A novel dual coordinate descent method for linear SVM with L1-and L2-loss functions that reaches an ε-accurate solution in O(log(1/ε)) iterations is presented.
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Generating Natural Language Adversarial Examples
TLDR
A black-box population-based optimization algorithm is used to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively.
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
TLDR
A data-augmentation approach is demonstrated that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by rule-based, feature-rich, and neural coreference systems in WinoBias without significantly affecting their performance on existing datasets.
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Learning Gender-Neutral Word Embeddings
TLDR
A novel training procedure for learning gender-neutral word embeddings that preserves gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence is proposed.
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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
TLDR
This work proposes to inject corpus-level constraints for calibrating existing structured prediction models and design an algorithm based on Lagrangian relaxation for collective inference to reduce the magnitude of bias amplification in multilabel object classification and visual semantic role labeling.
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A Transformer-based Approach for Source Code Summarization
TLDR
This work explores the Transformer model that uses a self-attention mechanism and has shown to be effective in capturing long-range dependencies in source code summarization, and shows that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
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Learning to Search Better than Your Teacher
TLDR
A new learning to search algorithm, LOLS, is provided, which does well relative to the reference policy, but additionally guarantees low regret compared to deviations from the learned policy: a local-optimality guarantee.
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