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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 ...
Abstract: Read paper: https://www.aclanthology.org/2021.findings-acl.446 Abstract: Defining a sophisticated action space for a dialog agent is essential for efficient training with reinforcement learning (RL). Recent work introduces discrete latent variables to use as an action space; however, a limitation is that a global vector can contain entangled information such as dialog act, sentence structure, and content. This sacrifices the flexibility of the response generation. In this paper, we propose phrase-level action reinforcement learning (PhraseRL), which allows the model to flexibly alter the sentence structure and content with the sequential action selection. Our model first learns to generate useful phrases during the supervised pre-training, and then further trained to form a response by rearranging the phrases with reinforcement learning. Experiments on the MultiWOZ dataset show that our model achieves competitive results with state-of-the-art models on automatic evaluation metrics, indicating that our ...
Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Neural Network; Semantics
URL: https://dx.doi.org/10.48448/ne1y-mg85
https://underline.io/lecture/26537-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 ...
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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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