Neural Network Joint Model
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Fast and Robust Neural Network Joint Models for Statistical Machine Translation
Recent work has shown success in us- ing neural network language models (NNLMs) as features in MT systems. Here, we present a novel formulation for a neural network joint model …...
How to Avoid Unwanted Pregnancies: Domain Adaptation using Neural Network Models
We present novel models for domain adap- tation based on the neural network joint model (NNJM). Our models maximize the cross entropy by regularizing the loss function with respect to …...
Using joint models or domain adaptation in statistical machine translation
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, namely the Operation Sequence …...
Adapting Grammatical Error Correction Based on the Native Language of Writers with Neural Network Joint Models
An important aspect for the task of grammat- ical error correction (GEC) that has not yet been adequately explored is adaptation based on the native language (L1) of writers, despite …...
An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation
The neural network joint model of transla- tion or NNJM (Devlin et al., 2014) combines source and target context to produce a power- ful translation feature. However, its softmax layer …...
QCRI’s Machine Translation Systems for IWSLT’16
AbstractThis paper describes QCRI’s machine translation systems for the IWSLT 2016 evaluation campaign. We participated in the Arabic→English and English→Arabic tracks. We built both Phrase-based and Neural machine translation models, …...