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Related Concept Videos

Language and Cognition01:27

Language and Cognition

375
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.
375
Language Development01:22

Language Development

395
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.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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Purposive Learning01:22

Purposive Learning

142
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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Chunking01:12

Chunking

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Chunking is a powerful cognitive technique that improves short-term memory retention by organizing information into smaller, more manageable units. The brain, limited by working memory capacity, can more easily process and store information when it is divided into "chunks" rather than presented as discrete, unrelated elements. Chunking is especially useful when dealing with large amounts of information, such as numerical sequences, words, or complex ideas.
The principle behind chunking...
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Related Experiment Video

Updated: Jul 19, 2025

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
08:32

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks

Published on: September 5, 2019

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What does Chinese BERT learn about syntactic knowledge?

Jianyu Zheng1, Ying Liu1

  • 1Department of Chinese Language and Literature, Tsinghua University, Haidian Distinct, Beijing, China.

Peerj. Computer Science
|August 7, 2023
PubMed
Summary

This study probes Chinese BERT, revealing its attention heads and hidden states encode syntactic knowledge. Fine-tuned models also preserve language structure, explaining BERT

Area of Science:

  • Natural Language Processing (NLP)
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Pre-trained language models like Bidirectional Encoder Representations from Transformers (BERT) excel in NLP tasks.
  • Research on BERT's learned knowledge is extensive for English but limited for Chinese.
  • Chinese BERT's syntactic knowledge acquisition remains under-explored due to its character-based writing system.

Purpose of the Study:

  • To investigate the syntactic knowledge embedded within Chinese BERT's attention heads and hidden states.
  • To analyze how fine-tuning affects the preservation of syntactic information in Chinese BERT for various NLP tasks.

Main Methods:

  • Employed probing methods to identify syntactic information in Chinese BERT's attention mechanisms and layer representations.
Keywords:
BERTChineseFine-tuneNLPSyntax

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  • Analyzed individual and combined attention heads for encoding specific and overall syntactic relations.
  • Examined fine-tuned Chinese BERT models across diverse tasks to assess structural information retention.

    • Main Results:

    • Specific attention heads and their combinations effectively encode syntactic relations in Chinese BERT.
    • Hidden representations across BERT layers contain varying degrees of syntactic information.
    • Fine-tuned Chinese BERT models demonstrate adaptation while retaining core language structures.

    Conclusions:

    • Chinese BERT possesses significant syntactic knowledge within its attention heads and hidden states.
    • The model's ability to retain syntactic structures post-fine-tuning contributes to its strong performance in Chinese NLP tasks.
    • Findings provide insights into the linguistic capabilities of BERT models for character-based languages.