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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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10
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 ...
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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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 ...
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