37 citations · 46 across the 3 of their papers we have counts for
12 papers
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference
Hai Hu, He Zhou, Zuoyu Tian +5
Multilingual transformers (XLM, mT5) have been shown to have remarkable transfer skills in zero-shot settings. Most transfer studies, however, rely on automatically translated reso…
Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie +16
We introduce Dynabench, an open-source platform for dynamic dataset creation and model benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the-loop datase…
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling
Yixin Nie, Mary Williamson, Mohit Bansal +2
To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a…
To what extent do human explanations of model behavior align with actual model behavior?
Grusha Prasad, Yixin Nie, Mohit Bansal +3
Given the increasingly prominent role NLP models (will) play in our lives, it is important for human expectations of model behavior to align with actual model behavior. Using Natur…
ConjNLI: Natural Language Inference Over Conjunctive Sentences
Swarnadeep Saha, Yixin Nie, Mohit Bansal
Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctiv…
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…