157 citations · 238 across the 9 of their papers we have counts for
9 papers
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Yu Cao, Dianqi Li, Meng Fang +4
We present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while main…
Phrase-level Textual Adversarial Attack with Label Preservation
Yibin Lei, Yu Cao, Dianqi Li +3
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.…
Contextualized Perturbation for Textual Adversarial Attack
Dianqi Li, Yizhe Zhang, Hao Peng +4
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of genera…
A Mixture of Heads is Better than Heads
Hao Peng, Roy Schwartz, Dianqi Li +1
Multi-head attentive neural architectures have achieved state-of-the-art results on a variety of natural language processing tasks. Evidence has shown that they are overparameteriz…
Contextual Text Style Transfer
Yu Cheng, Zhe Gan, Yizhe Zhang +3
We introduce a new task, Contextual Text Style Transfer - translating a sentence into a desired style with its surrounding context taken into account. This brings two key challenge…
Toward Interpretability of Dual-Encoder Models for Dialogue Response Suggestions
Yitong Li, Dianqi Li, Sushant Prakash +1
This work shows how to improve and interpret the commonly used dual encoder model for response suggestion in dialogue. We present an attentive dual encoder model that includes an a…
Domain Adaptive Text Style Transfer
Dianqi Li, Yizhe Zhang, Zhe Gan +4
Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In t…
Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning
Dianqi Li, Qiuyuan Huang, Xiaodong He +2
We study how to generate captions that are not only accurate in describing an image but also discriminative across different images. The problem is both fundamental and interesting…
Adversarial Ranking for Language Generation
Kevin Lin, Dianqi Li, Xiaodong He +2
Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit th…