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On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification
International Joint Conference on Artificial Intelligence (IJCAI), 2020
27 April 2020
Xin Liu
Jiefu Ou
Yangqiu Song
Xin Jiang
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Papers citing
"On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification"
40 / 40 papers shown
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Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition
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Prompt-based Logical Semantics Enhancement for Implicit Discourse Relation Recognition
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Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation Recognition
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Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation
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Michael Strube
198
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TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition
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Chao Liang
Bang Wang
165
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DiscoPrompt: Path Prediction Prompt Tuning for Implicit Discourse Relation Recognition
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Ginny Wong
Simon See
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Cheng Jiayang
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Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation
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Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations
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PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation
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A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond
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latent
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FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition
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ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
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316
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Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems
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Matt Grenander
Robert Belfer
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Iulian Serban
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Jackie C.K. Cheung
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Paolo Rota
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FastLR: Non-Autoregressive Lipreading Model with Integrate-and-Fire
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