Measuring user acceptability of machine translations to diagnose system errors

Hui, Bowen

doi:10.3115/1118794.1118803

Cited by 2 publications

(1 citation statement)

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“…In Hui (2002) a heuristic, task-based evaluation was designed to identify users’ responses to features of MT output such as conciseness and cohesiveness.…”

Section: Background: Mt Evaluation and User Preference Modelingmentioning

confidence: 99%

A conjoint analysis framework for evaluating user preferences in machine translation

Kirchhoff

Capurro

Turner

2013

Machine Translation

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Despite much research on machine translation (MT) evaluation, there is surprisingly little work that directly measures users’ intuitive or emotional preferences regarding different types of MT errors. However, the elicitation and modeling of user preferences is an important prerequisite for research on user adaptation and customization of MT engines. In this paper we explore the use of conjoint analysis as a formal quantitative framework to assess users’ relative preferences for different types of translation errors. We apply our approach to the analysis of MT output from translating public health documents from English into Spanish. Our results indicate that word order errors are clearly the most dispreferred error type, followed by word sense, morphological, and function word errors. The conjoint analysis-based model is able to predict user preferences more accurately than a baseline model that chooses the translation with the fewest errors overall. Additionally we analyze the effect of using a crowd-sourced respondent population versus a sample of domain experts and observe that main preference effects are remarkably stable across the two samples.

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“…In Hui (2002) a heuristic, task-based evaluation was designed to identify users’ responses to features of MT output such as conciseness and cohesiveness.…”

Section: Background: Mt Evaluation and User Preference Modelingmentioning

confidence: 99%