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1
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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2
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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3
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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4
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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5
Including Signed Languages in Natural Language Processing ...
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6
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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7
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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8
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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9
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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10
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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11
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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12
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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13
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
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14
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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15
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.501 Abstract: We study the task of long-form opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of information are needed to guide the generator to cover both subjective and objective content. To this end, we propose DYPLOC, a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. To enrich the generation with diverse content, we further propose to use large pre-trained models to predict relevant concepts and to generate claims. We experiment with two challenging tasks on newly collected datasets: (1) argument generation with Reddit ChangeMyView, and (2) writing articles using New York Times' Opinion section. Automatic evaluation shows that our model significantly outperforms competitive comparisons. Human judges further ...
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/xtrk-d266
https://underline.io/lecture/25720-dyploc-dynamic-planning-of-content-using-mixed-language-models-for-text-generation
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16
Automated Concatenation of Embeddings for Structured Prediction ...
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17
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus ...
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18
Code Generation from Natural Language with Less Prior Knowledge and More Monolingual Data ...
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19
Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification ...
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20
Scaling Within Document Coreference to Long Texts ...
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