266 citations · 1.8k across the 115 of their papers we have counts for
30 papers · 1 filter
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
Phillip Rust, Jonas Pfeiffer, Ivan Vulić +2
In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their m…
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych +1
Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity…
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes
Nils Reimers, Iryna Gurevych
Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no pr…
Generalizing Cross-Document Event Coreference Resolution Across Multiple Corpora
Michael Bugert, Nils Reimers, Iryna Gurevych
Cross-document event coreference resolution (CDCR) is an NLP task in which mentions of events need to be identified and clustered throughout a collection of documents. CDCR aims to…
Ranking Creative Language Characteristics in Small Data Scenarios
Julia Siekiera, Marius Köppel, Edwin Simpson +3
The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require…
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures
Nafise Sadat Moosavi, Marcel de Boer, Prasetya Ajie Utama +1
Existing NLP datasets contain various biases, and models tend to quickly learn those biases, which in turn limits their robustness. Existing approaches to improve robustness agains…