Work

Machine Translation with Many Manually Labeled Discourse Connectives

Year: 2013

Type: conference-paper

Abstract: The paper presents machine translation experiments from English to Czech with a large amount of manually annotated discourse connectives. The gold-standard discourse relation annotation leads to better translation performance in ranges of 4–60% for some ambiguous English connectives and helps to find correct syntactical constructs in Czech for less ambiguous connectives. Automatic scoring confirms the stability of the newly built discourse-aware translation systems. Error analysis and human translation evaluation point to the cases where the annotation was most and where less helpful.

Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne)

Authors: Thomas Meyer, Lucie Poláková

Institutions: Idiap Research Institute, Charles University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Univerzita Karlova v Praze, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Grantová Agentura České Republiky

Award: Improving the coherence of machine translation output by modeling intersentential relations

OpenAlex ID: W91670896