120 citations · 469 across the 55 of their papers we have counts for
12 papers · 1 filter
Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants
Max Bartolo, Tristan Thrush, Sebastian Riedel +3
In Dynamic Adversarial Data Collection (DADC), human annotators are tasked with finding examples that models struggle to predict correctly. Models trained on DADC-collected trainin…
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…
Spike-inspired Rank Coding for Fast and Accurate Recurrent Neural Networks
Alan Jeffares, Qinghai Guo, Pontus Stenetorp +1
Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. in the rank order in which neurons fire, whereas artificial neural networks (ANNs…
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…
Challenges in Generalization in Open Domain Question Answering
Linqing Liu, Patrick Lewis, Sebastian Riedel +1
Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with trai…
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…