Work

Perplexity minimization for translation model domain adaptation in statistical machine translation

Year: 2012

Type: conference-paper

Abstract: We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While techniques for domain adaptation of monolingual data can be borrowed for parallel data, we explore conceptual differences between translation model and language model domain adaptation and their effect on performance, such as the fact that translation models typically consist of several features that have different characteristics and can be optimized separately. We also explore adapting multiple (4–10) data sets with no a priori distinction between in-domain and out-of-domain data except for an in-domain development set.

Source: Zurich Open Repository and Archive (University of Zurich)

DOI: 10.5167/uzh-61712

Author: Sennrich, Rico

Institution: University of Zurich

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

OpenAlex ID: W2137698233