80 citations · 94 across the 8 of their papers we have counts for
10 papers
Towards Fine-grained Causal Reasoning and QA
Linyi Yang, Zhen Wang, Yuxiang Wu +2
Understanding causality is key to the success of NLP applications, especially in high-stakes domains. Causality comes in various perspectives such as enable and prevent that, despi…
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
Yuxiang Wu, Matt Gardner, Pontus Stenetorp +1
Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they a…
Training Adaptive Computation for Open-Domain Question Answering with Computational Constraints
Yuxiang Wu, Pasquale Minervini, Pontus Stenetorp +1
Adaptive Computation (AC) has been shown to be effective in improving the efficiency of Open-Domain Question Answering (ODQA) systems. However, current AC approaches require tuning…
PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them
Patrick Lewis, Yuxiang Wu, Linqing Liu +5
Open-domain Question Answering models which directly leverage question-answer (QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in terms of spee…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
Don't Read Too Much into It: Adaptive Computation for Open-Domain Question Answering
Yuxiang Wu, Sebastian Riedel, Pasquale Minervini +1
Most approaches to Open-Domain Question Answering consist of a light-weight retriever that selects a set of candidate passages, and a computationally expensive reader that examines…