# Informativeness across Interpreting Types: Implications for Language Shifts under Cognitive Load

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Distinctive Processes in CI and SI

#### 1.2. Entropic Measures in Exploring Language Texts

- (1)
- Could the quantitative indicators of word entropy, POS entropy, and repeat rate of words and word category distinguish CI and SI output texts?
- (2)
- If yes, which of the interpreting modes yields more informative and more heterogeneous output interpreting texts?
- (3)
- What are the implications regarding interpreting processing mechanisms underlying varied informativeness in SI and CI?

## 2. Materials and Methods

#### 2.1. Materials

_{(32)}= 1.258, p = 0.218). The Chinese source texts were aligned with English interpretations, and a total of 34 Chinese files of CI and SI were obtained. Table 1 is the overview of the corpus.

#### 2.2. Methods

_{i}stands for the token frequency of each word type W

_{i}in a text (or the frequency of each POS types), and V is the total number of types. For example, in the following sentence:

**The**most immediate

**and**important goal of our package plan is to reverse

**the**economic downturn

**and**maintain steady

**and**relatively fast growth.

_{1}, W

_{2},..., W

_{w}} in which W is the theoretical vocabulary size, and the word entropy of T can be calculated as [19]:

## 3. Results

#### 3.1. Comparison of Output Word Entropy between SI and CI

#### 3.2. Comparison of Output POS Entropy between SI and CI

#### 3.3. Comparison of Output Word RR and POS RR between SI and CI

^{2}= 0.3). Thus, the confounding factor of individual interpreting style was ruled out.

## 4. Discussions

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Word entropy analysis: (

**a**) scatter plot visualizing the relationship between input and output word entropy in SI and CI (the datapoints indicate word entropy of each text); (

**b**) descriptive statistics: input and output word entropy in SI and CI texts.

**Figure 2.**POS entropy analysis: (

**a**) scatter plot visualizing the relationship between input and output POS entropy in SI and CI (the datapoints indicate POS entropy of each text); (

**b**) descriptive statistics: input and output POS entropy in SI and CI texts.

**Figure 3.**Word RR analysis: (

**a**) scatter plot visualizing the relationship between input and output Word RR in SI and CI (the datapoints indicate word RR of each text); (

**b**) descriptive statistics: input and output Word RR in SI and CI texts.

**Figure 4.**POS RR analysis: (

**a**) scatter plot visualizing the relationship between input and output POS RR in SI and CI (the datapoints indicate POS RR of each text); (

**b**) descriptive statistics: input and output POS RR in SI and CI texts.

Sub-Corpora | Chinese/English | Text Count | Overall Size |
---|---|---|---|

CI | English | 17 | 86,529 |

Chinese | 17 | 65,590 | |

SI | English | 17 | 86,017 |

Chinese | 17 | 63,678 |

Predictor | Estimate | SE | t-Value | p | |
---|---|---|---|---|---|

Output word entropy | Intercept | 8.44 | 0.022 | 383.56 | <0.001 * |

Input word entropy | 0.77 | 0.091 | 8.43 | <0.001 * | |

Interpreting types | 0.12 | 0.035 | 3.40 | 0.002 * | |

Input word entropy × Interpreting types | 0.51 | 0.18 | 2.80 | 0.009 * |

Predictor | Estimate | SE | t-Value | p | |
---|---|---|---|---|---|

Output POS entropy | Intercept | 3.28 | 0.016 | 202.83 | <0.001 * |

Input POS entropy | 0.31 | 0.065 | 4.70 | <0.001 * | |

Interpreting types | 0.024 | 0.028 | 0.84 | 0.41 | |

Input POS entropy × Interpreting types | 0.44 | 0.13 | 3.35 | 0.003 * |

Predictor | Estimate | SE | t-Value | p | |
---|---|---|---|---|---|

Output word RR | Intercept | 0.01 | 0.00033 | 35.813 | <0.001 * |

Input POS entropy | 0.55 | 0.17 | 3.22 | 0.0031 * | |

Interpreting types | −0.0029 | 0.00061 | −4.69 | <0.001 * | |

Input POS entropy × Interpreting types | 0.57 | 0.34 | 1.67 | 0.11 |

Predictor | Estimate | SE | t-Value | p | |
---|---|---|---|---|---|

Output POS RR | Intercept | 0.13 | 0.0019 | 70.42 | <0.001 * |

Input POS entropy | 0.28 | 0.051 | 5.43 | <0.001 * | |

Interpreting types | −0.0035 | 0.0035 | −1.01 | 0.32 | |

Input POS entropy × Interpreting types | 0.43 | 0.1 | 4.22 | <0.001 * |

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Lin, Y.; Liang, J. Informativeness across Interpreting Types: Implications for Language Shifts under Cognitive Load. *Entropy* **2023**, *25*, 243.
https://doi.org/10.3390/e25020243

**AMA Style**

Lin Y, Liang J. Informativeness across Interpreting Types: Implications for Language Shifts under Cognitive Load. *Entropy*. 2023; 25(2):243.
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**Chicago/Turabian Style**

Lin, Yumeng, and Junying Liang. 2023. "Informativeness across Interpreting Types: Implications for Language Shifts under Cognitive Load" *Entropy* 25, no. 2: 243.
https://doi.org/10.3390/e25020243