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Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction ...
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Could you give me a hint? Generating inference graphs for defeasible reasoning ...
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Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction ...
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Could you give me a hint ? Generating inference graphs for defeasible reasoning ...
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Investigating Robustness of Dialog Models to Popular Figurative Language Constructs ...
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Measuring and Improving Consistency in Pretrained Language Models ...
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More Identifiable yet Equally Performant Transformers for Text Classification ...
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Improving Automated Evaluation of Open Domain Dialog via Diverse Reference Augmentation ...
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Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding ...
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SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers ...
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Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes ...
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StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer ...
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StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer ...
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Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes ...
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Extracting Implicitly Asserted Propositions in Argumentation ...
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Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ...
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On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT ...
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Abstract:
Contextualized word representations have become a driving force in NLP, motivating widespread interest in understanding their capabilities and the mechanisms by which they operate. Particularly intriguing is their ability to identify and encode conceptual abstractions. Past work has probed BERT representations (Devlin et al., 2019) for this competence, finding that BERT can correctly retrieve noun hypernyms in cloze tasks. In this work, we ask the question: do probing studies shed light on systematic knowledge in BERT representations? As a case study, we examine hypernymy knowledge encoded in BERT representations. In particular, we demonstrate through a simple consistency probe that the ability to correctly retrieve hypernyms in cloze tasks, as used in prior work, does not correspond to systematic knowledge in BERT. Our main conclusion is cautionary: even if BERT demonstrates high probing accuracy for a particular competence, it does not necessarily follow that BERT‘understands’ a concept, and it cannot be ...
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Keyword:
Computer and Information Science; Information and Knowledge Engineering; Intelligent System; Natural Language Processing; Neural Network
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URL: https://dx.doi.org/10.48448/sa1q-9g43 https://underline.io/lecture/6663-on-the-systematicity-of-probing-contextualized-word-representations-the-case-of-hypernymy-in-bert
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On aligning OpenIE extractions with Knowledge Bases: A case study
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Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
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In: Computational Linguistics, Vol 45, Iss 4, Pp 627-665 (2020) (2020)
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