Year: 2005
Type: conference-paper
Abstract: Bilingual word alignment forms the foundation of most approaches to statistical machine translation.Current word alignment methods are predominantly based on generative models.In this paper, we demonstrate a discriminative approach to training simple word alignment models that are comparable in accuracy to the more complex generative models normally used.These models have the the advantages that they are easy to add features to and they allow fast optimization of model parameters using small amounts of annotated data.
Author: Robert C. Moore
Institution: Microsoft (United States)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2124810114