Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …

Training a high-accuracy dependency parser requires a large treebank. However, these are
costly and time-consuming to build. We propose a learning method that needs less data …

Viable cross-lingual transfer critically depends on the availability of parallel texts. Shortage
of such resources imposes a development and evaluation bottleneck in multilingual …

The distributional hypothesis of Harris (1954), according to which the meaning of words is
evidenced by the contexts they occur in, has motivated several effective techniques for …

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 …

We present a new collection of treebanks with homogeneous syntactic dependency
annotation for six languages: German, English, Swedish, Spanish, French and Korean. To …

For languages with no annotated resources, unsupervised transfer of natural language
processing models such as named-entity recognition (NER) from resource-rich languages …

We train one multilingual model for dependency parsing and use it to parse sentences in
several languages. The parsing model uses (i) multilingual word clusters and …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

We present posterior regularization, a probabilistic framework for structured, weakly
supervised learning. Our framework efficiently incorporates indirect supervision via …