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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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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Multi-task learning approach for utilizing temporal relations in natural language understanding tasks.

Chae-Gyun Lim1, Young-Seob Jeong2, Ho-Jin Choi3

  • 1School of Computing, KAIST, Daejeon, 34141, South Korea.

Scientific Reports
|May 26, 2023
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Summary
This summary is machine-generated.

Integrating temporal relation extraction into multi-task learning significantly boosts natural language understanding (NLU) performance. This approach enhances models

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Natural language understanding (NLU) models often require understanding temporal information for accurate context processing.
  • Multi-task learning (MTL) aims to generalize model performance across various tasks.
  • Temporal relations are crucial for comprehending document content but are often overlooked in standard NLU tasks.

Purpose of the Study:

  • To propose and evaluate a novel multi-task learning technique that incorporates temporal relation extraction (TRE) into NLU task training.
  • To investigate how integrating TRE influences the generalized performance of NLU models.
  • To analyze the impact of TRE on NLU tasks across different languages, specifically Korean and English.

Main Methods:

  • Designed an MTL framework that includes a dedicated TRE task alongside existing NLU tasks.
  • Trained and evaluated the MTL model on both Korean and English datasets.
  • Analyzed performance variations by combining TRE with different NLU tasks.

Main Results:

  • Single-task TRE accuracy was 57.8% (Korean) and 45.1% (English).
  • Combined MTL approach improved TRE accuracy to 64.2% (Korean) and 48.7% (English).
  • Linguistic differences between Korean and English necessitate distinct task combinations for optimal TRE performance.

Conclusions:

  • Incorporating TRE into MTL enhances overall NLU model performance compared to individual task training.
  • The effectiveness of task combinations for improving TRE varies between Korean and English due to linguistic disparities.
  • MTL is a viable strategy for improving the extraction of temporal context in NLU.