In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Neural machine translation (NMT) offers a novel alternative formulation of translation that is
potentially simpler than statistical approaches. However to reach competitive performance …

It can be challenging to train multi-task neural networks that outperform or even match their
single-task counterparts. To help address this, we propose using knowledge distillation …

In cross-lingual transfer, NLP models over one or more source languages are applied to a
low-resource target language. While most prior work has used a single source model or a …

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich
contextual syntactic and even semantic meanings. This paper makes the first attempt to …

We investigate the problem of efficiently incorporating high-order features into neural graph-
based dependency parsing. Instead of explicitly extracting high-order features from …

Attention mechanisms have improved the performance of NLP tasks while allowing models
to remain explainable. Self-attention is currently widely used, however interpretability is …

Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have
demonstrated excellent results in question answering areas. However, due to the sheer …

Recurrent neural network grammars (RNNG) are a recently proposed probabilistic
generative modeling family for natural language. They show state-of-the-art language …

We present a deep neural architecture that parses sentences into three semantic
dependency graph formalisms. By using efficient, nearly arc-factored inference and a …