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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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  1. Home
  3. Reading To Translate Or Translating To Read? Modeling Translators' Eye Movements With Multilingual Pre-trained Models.
  1. Home
  3. Reading To Translate Or Translating To Read? Modeling Translators' Eye Movements With Multilingual Pre-trained Models.

Related Experiment Video

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading
05:54

Eye-tracking to Distinguish Comprehension-based and Oculomotor-based Regressive Eye Movements During Reading

Published on: October 18, 2018

Reading to Translate or Translating to Read? Modeling Translators' Eye Movements with Multilingual Pre-Trained

Yiyu Zhang1,2, Xiajing Yao1, Dechao Li2

  • 1School of Foreign Languages, China University of Geosciences (Wuhan), Wuhan 430074, China.

Journal of Eye Movement Research
|June 25, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Computational models explain translator reading patterns. Monolingual language model (LM) surprisal best predicts target reading time, while source reading involves encoder attention and LM surprisal. Production duration also uses NMT translation surprisal.

Keywords:
bilingual readingcomputational psycholinguisticseye-trackinglanguage modelneural machine translationpost-editingsurprisaltranslation process

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07:36

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Published on: November 30, 2018

Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Human-Computer Interaction

Background:

  • Translation and post-editing involve significant reading of bilingual texts.
  • Understanding translators' reading patterns is crucial for improving translation technologies.
  • The predictive power of computational models for these reading patterns remains unclear.

Purpose of the Study:

  • To identify which computational predictors from multilingual models best explain translator reading patterns.
  • To investigate these patterns across different translation tasks (translation vs. post-editing) and directions (en→zh vs. zh→en).
  • To compare the explanatory power of language model (LM) and neural machine translation (NMT) architectures.

Main Methods:

  • Recruited 26 native Chinese translators performing en→zh and zh→en translation and post-editing.
  • Collected 104 eye-tracking sessions measuring source reading time (TrtS), target reading time (TrtT), and production duration (Dur).
  • Derived predictors from LM and NMT models, including monolingual surprisal, translation surprisal, and attention features.

    • Main Results:

    • Language model (LM) surprisal was the strongest predictor of target reading time (TrtT).
    • Source reading time (TrtS) was best predicted by encoder self-attention combined with LM surprisal.
    • Target production duration (Dur) was influenced by both LM and NMT translation surprisal, indicating revision behavior.

    Conclusions:

    • While translation tasks are bilingual, cumulative reading time is primarily explained by monolingual LM surprisal.
    • Production duration incorporates NMT translation surprisal, reflecting the integration of machine translation output and revision.
    • Translation directionality had broader effects than task type, particularly on target-oriented measures.