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1
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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3
Comparative Error Analysis in Neural and Finite-state Models for Unsupervised Character-level Transduction ...
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4
Could you give me a hint ? Generating inference graphs for defeasible reasoning ...
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5
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs ...
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6
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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9
Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.185 Abstract: Every natural text is written in some style. Style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. One cannot form a complete understanding of a text without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides the benchmark corpus (XSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation. The benchmark contains text in 15 different styles under the proposed four theoretical groupings: figurative, personal, affective, and interpersonal groups. For valid evaluation, we collect an additional diagnostic set by annotating all 15 styles on the same text. Using XSLUE, we propose three interesting ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/5e1h-sn48
https://underline.io/lecture/25510-style-is-not-a-single-variable-case-studies-for-cross-stylistic-language-understanding
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10
SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers ...
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11
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes ...
Jo, Yohan; Bang, Seojin; Reed, Chris. - : arXiv, 2021
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12
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer ...
Lyu, Yiwei; Liang, Paul Pu; Pham, Hai. - : arXiv, 2021
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13
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer ...
NAACL 2021 2021; Hovy, Eduard; Liang, Paul Pu. - : Underline Science Inc., 2021
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14
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes ...
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15
Extracting Implicitly Asserted Propositions in Argumentation ...
Jo, Yohan; Visser, Jacky; Reed, Chris. - : arXiv, 2020
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16
Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance? ...
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17
Measuring Forecasting Skill from Text ...
Zong, Shi; Ritter, Alan; Hovy, Eduard. - : arXiv, 2020
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18
On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT ...
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19
On aligning OpenIE extractions with Knowledge Bases: A case study
Gashteovski, Kiril; Gemulla, Rainer; Kotnis, Bhushan. - : Association for Computational Linguistics (ACL), 2020
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20
Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
In: Computational Linguistics, Vol 45, Iss 4, Pp 627-665 (2020) (2020)
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