6 citations · 36 across the 23 of their papers we have counts for
13 papers · 1 filter
Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification
Ruixuan Tang, Hanjie Chen, Yangfeng Ji
Some recent works observed the instability of post-hoc explanations when input side perturbations are applied to the model. This raises the interest and concern in the stability of…
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Stephanie Schoch, Haifeng Xu, Yangfeng Ji
Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label det…
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…
REV: Information-Theoretic Evaluation of Free-Text Rationales
Hanjie Chen, Faeze Brahman, Xiang Ren +3
Generating free-text rationales is a promising step towards explainable NLP, yet evaluating such rationales remains a challenge. Existing metrics have mostly focused on measuring t…
Efficient NLP Model Finetuning via Multistage Data Filtering
Xu Ouyang, Shahina Mohd Azam Ansari, Felix Xiaozhu Lin +1
As model finetuning is central to the modern NLP, we set to maximize its efficiency. Motivated by redundancy in training examples and the sheer sizes of pretrained models, we explo…