32 citations · 71 across the 14 of their papers we have counts for
25 papers · 1 filter
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks
Yuxiang Wu, Yu Zhao, Baotian Hu +3
Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model tha…
Query Expansion Using Contextual Clue Sampling with Language Models
Linqing Liu, Minghan Li, Jimmy Lin +2
Query expansion is an effective approach for mitigating vocabulary mismatch between queries and documents in information retrieval. One recent line of research uses language models…
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…
Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Yihong Chen, Pasquale Minervini, Sebastian Riedel +1
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for mul…
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification
Maximilian Mozes, Max Bartolo, Pontus Stenetorp +2
Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validat…
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…