In recent years, natural language processing (NLP) has got great development with deep
learning techniques. In the sub-field of machine translation, a new approach named Neural …

Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …

The encoder-decoder framework for neural machine translation (NMT) has been shown
effective in large data scenarios, but is much less effective for low-resource languages. We …

Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but
when used to generate natural language their output tends to be overly generic, repetitive …

Recent work has shown success in using neural network language models (NNLMs) as
features in MT systems. Here, we present a novel formulation for a neural network joint …

We examine how various types of noise in the parallel training data impact the quality of
neural machine translation systems. We create five types of artificial noise and analyze how …

There has been a proliferation of recent work on SMT tuning algorithms capable of handling
larger feature sets than the traditional MERT approach. We analyze a number of these …

We offer a simple, effective, and scalable method for statistical machine translation
parameter tuning based on the pairwise approach to ranking (Herbrich et al., 1999). Unlike …

We present a set of experiments to demonstrate that deep recurrent neural networks (RNNs)
learn internal representations that capture soft hierarchical notions of syntax from highly …

Arabic Dialects present many challenges for machine translation, not least of which is the
lack of data resources. We use crowdsourcing to cheaply and quickly build Levantine …