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1
Priorless Recurrent Networks Learn Curiously ...
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2
Composing Byte-Pair Encodings for Morphological Sequence Classification ...
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3
Variation in Universal Dependencies annotation: A token based typological case study on adpossessive constructions ...
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4
Corpus evidence for word order freezing in Russian and German ...
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5
An analysis of language models for metaphor recognition ...
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6
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models ...
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7
Exhaustive Entity Recognition for Coptic - Challenges and Solutions ...
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8
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
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9
Attentively Embracing Noise for Robust Latent Representation in BERT ...
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10
Catching Attention with Automatic Pull Quote Selection ...
Abstract: To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to ...
Keyword: Computer and Information Science; Natural Language Processing; Neural Network
URL: https://underline.io/lecture/6135-catching-attention-with-automatic-pull-quote-selection
https://dx.doi.org/10.48448/xg5x-xh93
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11
Classifier Probes May Just Learn from Linear Context Features ...
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12
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
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13
Part 6 - Cross-linguistic Studies ...
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14
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information ...
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15
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs ...
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16
Autoencoding Improves Pre-trained Word Embeddings ...
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17
Exploring End-to-End Differentiable Natural Logic Modeling ...
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18
AutoMeTS: The Autocomplete for Medical Text Simplification. ...
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19
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English ...
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20
SemEval Task 6: DeftEval ...
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