Author | Search for: Razmara, Majid; Search for: Foster, George1; Search for: Sankaran, Baskaran; Search for: Sarkar, Anoop |
---|
Affiliation | - National Research Council of Canada
|
---|
Format | Text, Article |
---|
Conference | 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012), Jeju Island, Republic of Korea, 8-14 July, 2012 |
---|
Abstract | Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain. We propose a novel approach, ensemble decoding, which combines a number of translation systems dynamically at the decoding step. In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain. Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation. |
---|
Publication date | 2012-07 |
---|
In | |
---|
Language | English |
---|
Peer reviewed | Yes |
---|
NPARC number | 20494947 |
---|
Export citation | Export as RIS |
---|
Report a correction | Report a correction (opens in a new tab) |
---|
Record identifier | 3a79c192-af06-4ff4-84a8-fbc03003c772 |
---|
Record created | 2012-08-16 |
---|
Record modified | 2020-04-21 |
---|