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Hits 401 – 420 of 20.397

401
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering ...
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402
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets ...
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403
Fine-Grained Spatial Information Extraction in Radiology as Two-turn Question Answering ...
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404
6B: Resources and Evaluation #1 ...
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405
Dynamic Contextualized Word Embeddings ...
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406
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes ...
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407
Hierarchy-aware Label Semantics Matching Network for Hierarchical Text Classification ...
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408
Towards Zero-Shot Knowledge Distillation for Natural Language Processing ...
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409
Corrected CBOW Performs as well as Skip-gram ...
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410
BERTAC: Enhancing Transformer-based Language Models with Adversarially Pretrained Convolutional Neural Networks ...
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411
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations ...
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412
Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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413
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting ...
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414
PSED: A Dataset for Selecting Emphasis in Presentation Slides ...
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415
Benchmarking Neural Topic Models: An Empirical Study ...
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416
Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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417
Meta Distant Transfer Learning for Pre-trained Language Models ...
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418
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques ...
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419
Plot and Rework: Modeling Storylines for Visual Storytelling ...
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420
SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.136 Abstract: In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
URL: https://underline.io/lecture/25787-saroco-detecting-satire-in-a-novel-romanian-corpus-of-news-articles
https://dx.doi.org/10.48448/ygzc-s715
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