Year: 2014
Type: conference-paper
Abstract: This paper addresses the problem of selecting adequate training sentence pairs from a mix-ofdomains parallel corpus for a translation task represented by a small in-domain parallel corpus.We propose a novel latent domain translation model which includes domain priors, domaindependent translation models and language models.The goal of learning is to estimate the probability of a sentence pair in mix-domain corpus to be in-or out-domain using in-domain corpus statistics as prior.We derive an EM training algorithm and provide solutions for estimating out-domain models (given only in-and mix-domain data).We report on experiments in data selection (intrinsic) and machine translation (extrinsic) on a large parallel corpus consisting of a mix of a rather diverse set of domains.Our results show that our latent domain invitation approach outperforms the existing baselines significantly.We also provide analysis of the merits of our approach relative to existing approaches.
Authors: Cuong Hoang, Khalil Sima’an
Institution: University of Amsterdam
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W2350508517