5 citations · 17 across the 10 of their papers we have counts for
11 papers · 1 filter
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models
Hannah Chen, Yangfeng Ji, David Evans
Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different pr…
Pre-trained Sentence Embeddings for Implicit Discourse Relation Classification
Murali Raghu Babu Balusu, Yangfeng Ji, Jacob Eisenstein
Implicit discourse relations bind smaller linguistic units into coherent texts. Automatic sense prediction for implicit relations is hard, because it requires understanding the sem…
White-box Testing of NLP models with Mask Neuron Coverage
Arshdeep Sekhon, Yangfeng Ji, Matthew B. Dwyer +1
Recent literature has seen growing interest in using black-box strategies like CheckList for testing the behavior of NLP models. Research on white-box testing has developed a numbe…
Pathologies of Pre-trained Language Models in Few-shot Fine-tuning
Hanjie Chen, Guoqing Zheng, Ahmed Hassan Awadallah +1
Although adapting pre-trained language models with few examples has shown promising performance on text classification, there is a lack of understanding of where the performance ga…
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors
Wanyu Du, Jianqiao Zhao, Liwei Wang +1
Generating high quality texts with high diversity is important for many NLG applications, but current methods mostly focus on building deterministic models to generate higher quali…
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
Hanjie Chen, Yangfeng Ji
Neural language models show vulnerability to adversarial examples which are semantically similar to their original counterparts with a few words replaced by their synonyms. A commo…