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Related Concept Videos

Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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CRaDLe: Deep code retrieval based on semantic Dependency Learning.

Wenchao Gu1, Zongjie Li2, Cuiyun Gao2

  • 1The Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces CRaDLe, a new method for code retrieval that uses statement-level dependency learning to bridge the semantic gap between natural language queries and code snippets. CRaDLe significantly improves code retrieval accuracy over existing methods.

Keywords:
Code retrievalDependency learningNeural networkSemantic dependency

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Code retrieval is crucial for programmers reusing open-source code snippets.
  • A significant challenge is the semantic gap between natural language queries and code.
  • Neural networks are increasingly used to learn semantic matching for code retrieval.

Purpose of the Study:

  • To propose CRaDLe, a novel approach for code retrieval using statement-level semantic dependency learning.
  • To address the semantic gap in code retrieval by incorporating structural code information.

Main Methods:

  • CRaDLe distills code representations by fusing statement-level dependency and semantic information.
  • It learns a unified vector representation for code and description pairs.
  • The approach models the matching relationship between queries and code snippets.

Main Results:

  • Comprehensive experiments on real-world datasets demonstrate CRaDLe's effectiveness.
  • The proposed approach accurately retrieves relevant code snippets for given queries.
  • CRaDLe significantly outperforms state-of-the-art code retrieval methods.

Conclusions:

  • Statement-level dependency information is valuable for capturing code semantics in retrieval.
  • CRaDLe offers a promising new direction for improving code retrieval systems.
  • The method effectively bridges the semantic gap, enhancing retrieval performance.