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1
Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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2
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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3
Bird’s Eye: Probing for Linguistic Graph Structures with a Simple Information-Theoretic Approach ...
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4
10D: Phonology, Morphology and Word Segmentation #1 ...
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5
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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6
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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7
18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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8
The Match-Extend Serialization Algorithm in Multiprecedence ...
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9
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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10
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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11
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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12
Including Signed Languages in Natural Language Processing ...
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13
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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14
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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15
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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16
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
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17
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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18
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.95 Abstract: To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries. In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations. To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mapped-query). Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotation-aware attention is applied on top of history queries to highlight the indicator words. Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models. ...
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/xprq-jp17
https://underline.io/lecture/25710-quotation-recommendation-and-interpretation-based-on-transformation-from-queries-to-quotations
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19
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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20
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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