5 citations · 20 across the 12 of their papers we have counts for
4 papers · 1 filter
What Can We Learn from Collective Human Opinions on Natural Language Inference Data?
Yixin Nie, Xiang Zhou, Mohit Bansal
Despite the subjective nature of many NLP tasks, most NLU evaluations have focused on using the majority label with presumably high agreement as the ground truth. Less attention ha…
Deep Reinforcement Learning for On-line Dialogue State Tracking
Zhi Chen, Lu Chen, Xiang Zhou +1
Dialogue state tracking (DST) is a crucial module in dialogue management. It is usually cast as a supervised training problem, which is not convenient for on-line optimization. In…
Towards Robustifying NLI Models Against Lexical Dataset Biases
Xiang Zhou, Mohit Bansal
While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting…
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions
Xiang Zhou, Yixin Nie, Hao Tan +1
We find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable. This raises…