120 citations · 469 across the 55 of their papers we have counts for
8 papers · 1 filter
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…
Learning Reasoning Strategies in End-to-End Differentiable Proving
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp +2
Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theor…
Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets
Patrick Lewis, Pontus Stenetorp, Sebastian Riedel
Ideally Open-Domain Question Answering models should exhibit a number of competencies, ranging from simply memorizing questions seen at training time, to answering novel question f…
AxCell: Automatic Extraction of Results from Machine Learning Papers
Marcin Kardas, Piotr Czapla, Pontus Stenetorp +4
Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine le…
Frequency-Guided Word Substitutions for Detecting Textual Adversarial Examples
Maximilian Mozes, Pontus Stenetorp, Bennett Kleinberg +1
Recent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show th…
Benchmarking Machine Reading Comprehension: A Psychological Perspective
Saku Sugawara, Pontus Stenetorp, Akiko Aizawa
Machine reading comprehension (MRC) has received considerable attention as a benchmark for natural language understanding. However, the conventional task design of MRC lacks explai…