Abstract Language use is known to be influenced by personality traits as well as by
sociodemographic characteristics such as age or mother tongue. As a result, it is possible to …

Neural machine translation (NMT) has been prominent in many machine translation tasks.
However, in some domain-specific tasks, only the corpora from similar domains can improve …

Abstract Domain adaptation is an active field of research in statistical machine translation
(SMT), but so far most work has ignored the distinction between the topic and genre of …

In this paper, we propose a method which uses semi-supervised convolutional neural
networks (CNNs) to select in-domain training data for statistical machine translation. This …

In this paper, we present evaluation corpora covering four genres for four language pairs
that we harvested from the web in an automated fashion. We use these multi-genre …

We present novel models for domain adaptation based on the neural network joint model
(NNJM). Our models maximize the cross entropy by regularizing the loss function with …

Studies have established that hybrid models outperform single models. The particle swarm
algorithm (PSO)-based PID (proportional-integral-derivative) controller control system is …

Joint models have recently shown to improve the state-of-the-art in machine translation (MT).
We apply EM-based mixture modeling and data selection techniques using two joint models …

Although more additional corpora are now available for Statistical Machine Translation
(SMT), only the ones which belong to the same or similar domains with the original corpus …

Existing work on domain adaptation for statistical machine translation has consistently
assumed access to a small sample from the test distribution (target domain) at training time …