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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
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing ...
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5
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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6
10D: Phonology, Morphology and Word Segmentation #1 ...
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7
Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems ...
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8
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check ...
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9
19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 2 ...
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18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology - Part 1 ...
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11
The Match-Extend Serialization Algorithm in Multiprecedence ...
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12
Recognizing Reduplicated Forms: Finite-State Buffered Machines ...
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13
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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14
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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15
Including Signed Languages in Natural Language Processing ...
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16
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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17
The Reading Machine: a Versatile Framework for Studying Incremental Parsing Strategies ...
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18
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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19
An FST morphological analyzer for the Gitksan language ...
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20
Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.279 Abstract: How does the input segmentation of pretrained language models (PLMs) affect their interpretations of complex words? We present the first study investigating this question, taking BERT as the example PLM and focusing on its semantic representations of English derivatives. We show that PLMs can be interpreted as serial dual-route models, i.e., the meanings of complex words are either stored or else need to be computed from the subwords, which implies that maximally meaningful input tokens should allow for the best generalization on new words. This hypothesis is confirmed by a series of semantic probing tasks on which DelBERT (Derivation leveraging BERT), a model with derivational input segmentation, substantially outperforms BERT with WordPiece segmentation. Our results suggest that the generalization capabilities of PLMs could be further improved if a morphologically-informed vocabulary of input tokens were used. ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Morphology; Neural Network; Semantics
URL: https://underline.io/lecture/25622-superbizarre-is-not-superb-derivational-morphology-improves-bert's-interpretation-of-complex-words
https://dx.doi.org/10.48448/0kb6-8w78
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