2 |
Character Alignment in Morphologically Complex Translation Sets for Related Languages ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Composing Byte-Pair Encodings for Morphological Sequence Classification ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Variation in Universal Dependencies annotation: A token based typological case study on adpossessive constructions ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Corpus evidence for word order freezing in Russian and German ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Exhaustive Entity Recognition for Coptic - Challenges and Solutions ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Attentively Embracing Noise for Robust Latent Representation in BERT ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
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
|
|
BASE
|
|
Hide details
|
|
13 |
Classifier Probes May Just Learn from Linear Context Features ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Seeing the world through text: Evaluating image descriptions for commonsense reasoning in machine reading comprehension ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Manifold Learning-based Word Representation Refinement Incorporating Global and Local Information ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
HMSid and HMSid2 at PARSEME Shared Task 2020: Computational Corpus Linguistics and unseen-in-training MWEs ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Multi-dialect Arabic BERT for Country-level Dialect Identification ...
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Exploring End-to-End Differentiable Natural Logic Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|