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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-short.69 Abstract: Subword segmentation algorithms have been a \textit{de facto} choice when building neural machine translation systems. However, most of them need to learn a segmentation model based on some heuristics, which may produce sub-optimal segmentation. This can be problematic in some scenarios when the target language has rich morphological changes or there is not enough data for learning compact composition rules. Translating at fully character level has the potential to alleviate the issue, but empirical performances of character-based models has not been fully explored. In this paper, we present an in-depth comparison between character-based and subword-based NMT systems under three settings: translating to typologically diverse languages, training with low resource, and adapting to unseen domains. Experiment results show strong competitiveness of character-based models. Further analyses show that compared to subword-based 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/mz1t-5939
https://underline.io/lecture/25626-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 ...
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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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