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1
Marginal Utility Diminishes: Exploring the Minimum Knowledge for BERT Knowledge Distillation ...
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2
Prevent the Language Model from being Overconfident in Neural Machine Translation ...
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3
Modeling Bilingual Conversational Characteristics for Neural Chat Translation ...
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4
Selective Knowledge Distillation for Neural Machine Translation ...
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5
Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition ...
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6
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation ...
Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.394 Abstract: Due to the great potential in facilitating software development, code generation has attracted increasing attention recently. Generally, dominant models are Seq2Tree models, which convert the input natural language description into a sequence of tree-construction actions corresponding to the pre-order traversal of an Abstract Syntax Tree (AST). However, such a traversal order may not be suitable for handling all multi-branch nodes. In this paper, we propose to equip the Seq2Tree model with a context-based Branch Selector, which is able to dynamically determine optimal expansion orders of branches for multi-branch nodes. Particularly, since the selection of expansion orders is a non-differentiable multi-step operation, we optimize the selector through reinforcement learning, and formulate the reward function as the difference of model losses obtained through different expansion orders. Experimental results and in-depth analysis on several ...
URL: https://dx.doi.org/10.48448/s35r-p284
https://underline.io/lecture/25808-exploring-dynamic-selection-of-branch-expansion-orders-for-code-generation
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