20 citations · 44 across the 5 of their papers we have counts for
10 papers
Distribution inference risks: Identifying and mitigating sources of leakage
Valentin Hartmann, Léo Meynent, Maxime Peyrard +3
A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inferen…
Better than Average: Paired Evaluation of NLP Systems
Maxime Peyrard, Wei Zhao, Steffen Eger +1
Evaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances. In this work, we question the use of aver…
Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?
Maxime Peyrard, Beatriz Borges, Kristina Gligorić +1
The automatic detection of humor poses a grand challenge for natural language processing. Transformer-based systems have recently achieved remarkable results on this task, but they…
KLearn: Background Knowledge Inference from Summarization Data
Maxime Peyrard, Robert West
The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver. So far, summarization rese…
Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis
Kristina Gligorić, Manoel Horta Ribeiro, Martin Müller +5
Timely access to accurate information is crucial during the COVID-19 pandemic. Prompted by key stakeholders' cautioning against an "infodemic", we study information sharing on Twit…
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
Wei Zhao, Goran Glavaš, Maxime Peyrard +3
Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual simil…