Legal English is a sublanguage that is important for everyone but not for everyone to understand.
Prior works have demonstrated that a low-resource language pair can benefit from multilingual machine translation (MT) systems, which rely on many language pairs' joint training.
Among the six challenges of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is considered the most severe one, especially in translation of low-resource languages.
While translating between East Asian languages, many works have discovered clear advantages of using characters as the translation unit.
Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data.
In this procedure, a model is first trained on a source domain data and then fine-tuned on a small set of target domain utterances under the guidance of two proposed critics.
Neural machine translation (NMT) systems have recently obtained state-of-the art in many machine translation systems between popular language pairs because of the availability of data.
Natural language generation (NLG) is an important component in spoken dialogue systems.
Natural language generation (NLG) plays a critical role in spoken dialogue systems.
Natural language generation (NLG) is a critical component in a spoken dialogue system.