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1
Characterizing Idioms: Conventionality and Contingency ...
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2
Optimizing Deeper Transformers on Small Datasets ...
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3
Textual Time Travel: A Temporally Informed Approach to Theory of Mind ...
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4
Modeling Event Plausibility with Consistent Conceptual Abstraction ...
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5
ADEPT: An Adjective-Dependent Plausibility Task ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.553 Abstract: A false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors. While correctly interpreting and assessing the effects of such adjective-noun pairs (e.g., false key) on the plausibility of given events (e.g., opening doors) underpins many natural language understanding tasks, doing so often requires a significant degree of world knowledge and common-sense reasoning. We introduce ADEPT -- a large-scale semantic plausibility task consisting of over 16 thousand sentences that are paired with slightly modified versions obtained by adding an adjective to a noun. Overall, we find that while the task appears easier for human judges (85% accuracy), it proves more difficult for transformer-based models like RoBERTa (71% accuracy). Our experiments also show that neither the adjective itself nor its taxonomic class suffice in determining the correct plausibility judgement, emphasizing the ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/jg07-gp92
https://underline.io/lecture/25791-adept-an-adjective-dependent-plausibility-task
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6
An Analysis of Dataset Overlap on Winograd-Style Tasks ...
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7
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT ...
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8
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms ...
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9
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization ...
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10
Leveraging Lexical Resources for Learning Entity Embeddings in Multi-Relational Data ...
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11
Distributional Semantics for Robust Automatic Summarization
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