ESR11

Adapting to All Domains at Once: Rewarding Domain Invariance in SMT

Hoang Cuong, Khalil Sima'an and Ivan Titov (2016) Adapting to All Domains at Once: Rewarding Domain Invariance in SMT. Transactions of the Association for Computational Linguistics (TACL)

Latent Domain Translation Models in Mix-of-Domains Haystack

Submitted by c.orasan on Wed, 25/11/2015 - 12:13

Based on the work by Hoang Cuong with Khalil Sima’an at COLING 2014 (Latent Domain Translation Models in Mix-of-Domains Haystack), a data selection tool for Machine Translation was implemented by Amir Kamran. The developed tool is available at the following Github repository: https://github.com/amirkamran/InvitationModel

Adapting to All Domains at Once: Rewarding Domain Invariance in SMT

Latent Domain Translation Models in Mix-of-Domains Haystack

Latent Domain Word Alignment for Heterogeneous Corpora

Latent Domain Phrase-based Models for Adaptation

Latent Domain Translation Models in Mix-of-Domains Haystack