Work

Combination of Machine Translation Systems via Hypothesis Selection from Combined n-best lists

Year: 2008

Type: conference-paper

Abstract: Different approaches in machine translation achieve similar translation quality with a variety of translations in the output. Recently it has been shown, that it is possible to leverage the individual strengths of various systems and improve the overall translation quality by combining translation outputs. In this paper we present a method of hypothesis selection which is relatively simple compared to system combination methods which construct a synthesis of the input hypotheses. Our method uses information from n-best lists from several MT systems and features on the sentence level which are independent from the MT systems involved to improve the translation quality.

Source: Conference of the Association for Machine Translation in the Americas

DOI: 10.5445/ir/1000166369

Authors: Hildebrand, Almut Silja, Vogel, Stephan

Institution: Carnegie Mellon University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2168733445

FWCI: 5.582

Cites: 15

Cited by: 58

Related to: 10