activity
20172026
most citedBPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages

129 citations · 148 across the 20 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

cs.CL2019★ 5 cited

When Choosing Plausible Alternatives, Clever Hans can be Clever

Pride Kavumba, Naoya Inoue, Benjamin Heinzerling +3

Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements i…

cs.CL2019

Riposte! A Large Corpus of Counter-Arguments

Paul Reisert, Benjamin Heinzerling, Naoya Inoue +2

Constructive feedback is an effective method for improving critical thinking skills. Counter-arguments (CAs), one form of constructive feedback, have been proven to be useful for c…

cs.CL2019★ 2 cited

On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages

Yi Zhu, Benjamin Heinzerling, Ivan Vulić +3

Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value sh…

cs.CL2019

Fine-Grained Entity Typing in Hyperbolic Space

Federico López, Benjamin Heinzerling, Michael Strube

How can we represent hierarchical information present in large type inventories for entity typing? We study the ability of hyperbolic embeddings to capture hierarchical relations b…

cs.CL2019

Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation

Benjamin Heinzerling, Michael Strube

Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of p…