activity
20152023
most citedTowards Scalable and Reliable Capsule Networks for Challenging NLP Applications

30 citations · 225 across the 39 of their papers we have counts for

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Showing 2020 · cs.CLShow all

7 papers · 2 filters

cs.CL2020

Probing Multilingual BERT for Genetic and Typological Signals

Taraka Rama, Lisa Beinborn, Steffen Eger

We probe the layers in multilingual BERT (mBERT) for phylogenetic and geographic language signals across 100 languages and compute language distances based on the mBERT representat…

cs.CL2020

Vec2Sent: Probing Sentence Embeddings with Natural Language Generation

Martin Kerscher, Steffen Eger

We introspect black-box sentence embeddings by conditionally generating from them with the objective to retrieve the underlying discrete sentence. We perceive of this as a new unsu…

cs.CL2020★ 4 cited

From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks

Steffen Eger, Yannik Benz

Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mos…

cs.CL2020

Inducing Language-Agnostic Multilingual Representations

Wei Zhao, Steffen Eger, Johannes Bjerva +1

Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world. However, they currently require large pretraining…

cs.CL2020★ 4 cited

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…

cs.CL2020★ 14 cited

SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization

Yang Gao, Wei Zhao, Steffen Eger

We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc…