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Related Concept Videos

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

Improving Translational Accuracy

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...

Translation

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of...

Translation

Translation

Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...

Translation

Translation

Translation

Translation

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A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation.

Katrin Kirchhoff¹, Daniel Capurro², Anne M Turner³

¹Department of Electrical Engineering, University of Washington, Seattle, WA, 98195, USA, Tel.: +1-206-616-5494, , katrin@ee.washington.edu.

Machine Translation : MT

|April 1, 2014

Summary

This summary is machine-generated.

This study reveals users dislike word order errors most in machine translation (MT). Conjoint analysis accurately predicts preferences, showing stability between crowd-sourced and expert evaluators for MT quality assessment.

Keywords:

evaluation machine translation preference elicitation user modeling

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Area of Science:

Natural Language Processing
Human-Computer Interaction
Computational Linguistics

Background:

Machine translation (MT) evaluation lacks research on user emotional preferences for error types.
Understanding user preferences is crucial for adapting and customizing MT engines.
Existing MT evaluation methods do not fully capture user-sentiments towards translation inaccuracies.

Purpose of the Study:

To formally assess users' relative preferences for different machine translation error types using conjoint analysis.
To model user preferences for MT output, specifically for public health documents translated from English to Spanish.
To compare the predictive accuracy of a conjoint analysis model against a baseline error-counting model.

Main Methods:

Conjoint analysis was employed as a quantitative framework to evaluate user preferences.
The study analyzed machine translation output for public health documents (English to Spanish).
Respondent populations included crowd-sourced individuals and domain experts to assess preference stability.

Main Results:

Word order errors were identified as the most dispreferred error type in machine translation.
User preferences were ranked, with word sense, morphological, and function word errors following.
The conjoint analysis model demonstrated superior prediction accuracy compared to a baseline model.
Key preference patterns remained consistent across both crowd-sourced and expert respondent groups.

Conclusions:

Conjoint analysis provides a robust method for quantifying user preferences in machine translation evaluation.
Addressing specific error types, particularly word order, is critical for improving user satisfaction with MT.
The stability of preferences across different evaluator groups suggests generalizability of the findings for MT quality assessment.