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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
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation ...
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4
Correcting Chinese Spelling Errors with Phonetic Pre-training ...
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5
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction ...
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6
Including Signed Languages in Natural Language Processing ...
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7
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
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8
To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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9
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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10
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation ...
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11
Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors ...
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12
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification ...
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13
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations ...
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14
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements ...
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15
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages ...
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16
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding ...
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17
Towards Protecting Vital Healthcare Programs by Extracting Actionable Knowledge from Policy ...
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18
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation ...
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19
Automated Concatenation of Embeddings for Structured Prediction ...
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20
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus ...
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