Work

Using Sense-labeled Discourse Connectives for Statistical Machine Translation

Year: 2012

Type: conference-paper

Abstract: This article shows how the automatic disambiguation of discourse connectives can improve Statistical Machine Translation (SMT) from English to French. Connectives are firstly disambiguated in terms of the discourse relation they signal between segments. Several classifiers trained using syntactic and semantic features reach state-of-the-art performance, with F1 scores of 0.6 to 0.8 over thirteen ambiguous English connectives. Labeled connectives are then used into SMT systems either by modifying their phrase table, or by training them on labeled corpora. The best modified SMT systems improve the translation of connectives without degrading BLEU scores. A threshold-based SMT system using only high-confidence labels improves BLEU scores by 0.2–0.4 points.

Source: Infoscience (Ecole Polytechnique Fédérale de Lausanne)

Authors: Thomas Meyer, Andréi Popescu-Belis

Institution: Idiap Research Institute

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

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

OpenAlex ID: W112038384

FWCI: 6.376

Cites: 27

Cited by: 60

Related to: 20