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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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Abstract:
Read the paper on the folowing link: https://www.aclweb.org/anthology/2021.naacl-main.171/ Abstract: Text style transfer aims to controllably generate text with targeted stylistic changes while maintaining core meaning from the source sentence constant. Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e.g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence. In this paper, we introduce a large-scale benchmark, StylePTB, with (1) paired sentences undergoing 21 fine-grained stylistic changes spanning atomic lexical, syntactic, semantic, and thematic transfers of text, as well as (2) compositions of multiple transfers which allow modeling of fine-grained stylistic changes as building blocks for more complex, high-level transfers. By benchmarking existing methods on StylePTB, we find that they struggle to model fine-grained changes and ...
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Keyword:
Artificial Intelligence; Computer Science and Engineering; Intelligent System; Machine Learning; Natural Language Processing
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URL: https://underline.io/lecture/19980-styleptb-a-compositional-benchmark-for-fine-grained-controllable-text-style-transfer https://dx.doi.org/10.48448/pefh-e473
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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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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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