Much recent progress in applications of machine learning models to NLP has been driven
by benchmarks that evaluate models across a wide variety of tasks. However, these broad …

Recent years have seen an exponential growth (+ 98% in 2022 wrt the previous year) of the
number of research articles in the few-shot learning field, which aims at training machine …

Cross-lingual transfer, where a high-resource transfer language is used to improve the
accuracy of a low-resource task language, is now an invaluable tool for improving …

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

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

Natural language understanding (NLU) in the context of goal-oriented dialog systems
typically includes intent classification and slot labeling tasks. Existing methods to expand an …

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 …

We show that it is possible to reliably discriminate whether a syntactic construction is meant
literally or metaphorically using lexical semantic features of the words that participate in the …

As user demand scales for intelligent personal assistants (IPAs) such as Apple's Siri,
Google's Google Now, and Microsoft's Cortana, we are approaching the computational limits …