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USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation ...
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Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations ...
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Towards Explainable Evaluation Metrics for Natural Language Generation ...
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End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset ...
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BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks ...
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Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ...
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Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ...
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Inducing Language-Agnostic Multilingual Representations ...
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Probing Multilingual BERT for Genetic and Typological Signals ...
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On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ...
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How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation ...
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Vec2Sent: Probing Sentence Embeddings With Natural Language Generation ...
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From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks ...
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Abstract:
Adversarial attacks are label-preserving modifications to inputs of machine learning classifiers designed to fool machines but not humans. Natural Language Processing (NLP) has mostly focused on high-level attack scenarios such as paraphrasing input texts. We argue that these are less realistic in typical application scenarios such as in social media, and instead focus on low-level attacks on the character-level. Guided by human cognitive abilities and human robustness, we propose the first large-scale catalogue and benchmark of low-level adversarial attacks, which we dub Zéroe, encompassing nine different attack modes including visual and phonetic adversaries. We show that RoBERTa, NLP's current workhorse, fails on our attacks. Our dataset provides a benchmark for testing robustness of future more human-like NLP models. ... : Authors accidentally in wrong order; cannot be undone due to conference constraints. Accepted for publication at AACL 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2010.05648 https://arxiv.org/abs/2010.05648
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On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation
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On aligning OpenIE extractions with Knowledge Bases: A case study
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Semantic Change and Emerging Tropes In a Large Corpus of New High German Poetry ...
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Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! ...
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What is the Essence of a Claim? Cross-Domain Claim Identification ...
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