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Found 11 papers, 6 papers with code
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers.
Phrase-level Textual Adversarial Attack with Label Preservation
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.
Towards Robust and Efficient Contrastive Textual Representation Learning
There has been growing interest in representation learning for text data, based on theoretical arguments and empirical evidence.
Contextualized Perturbation for Textual Adversarial Attack
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness.
A Mixture of h - 1 Heads is Better than h Heads
Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks.
A Mixture of $h-1$ Heads is Better than $h$ Heads
Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks.
Contextual Text Style Transfer
To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.
Toward Interpretability of Dual-Encoder Models for Dialogue Response Suggestions
To improve the interpretability in the dual encoder models, we design a novel regularization loss to minimize the mutual information between unimportant words and desired labels, in addition to the original attention method, so that important words are emphasized while unimportant words are de-emphasized.
Domain Adaptive Text Style Transfer
These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training.
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
By contrasting with human-written captions and image-mismatched captions, the caption generator effectively exploits the inherent characteristics of human languages, and generates more discriminative captions.
Adversarial Ranking for Language Generation
Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group.
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