Work

Parser Accuracy in Quality Estimation of Machine Translation: A Tree Kernel Approach

Year: 2013

Type: conference-paper

Abstract: We report on experiments designed to investigate the role of syntactic features in the task of quality estimation for machine translation, focusing on the effect of parser accuracy. Tree kernels are used to predict the segment-level BLEU score of English- French translations. In order to examine the effect of the accuracy of the parse tree on the accuracy of the quality estimation system, we experiment with various pars- ing systems which differ substantially with respect to their Parseval f-scores. We find that it makes very little difference which system we choose to use in the quality estimation task – this effect is particularly apparent for source-side English parse trees.

Source: Dublin City University Open Access Institutional Repository (Dublin City University)

Authors: Rasoul Kaljahi, Jennifer Foster, Raphaël Rubino, Johann Roturier, Fred Hollowood

Institutions: Dublin City University, NortonLifeLock (United States)

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Dublin City University, Irish Research Council

OpenAlex ID: W179033058

FWCI: 1.36

Cites: 27

Cited by: 6

Related to: 10