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 …

We automatically create enormous, free and multilingual silver-standard training annotations
for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner …

Although over 100 languages are supported by strong off-the-shelf machine translation
systems, only a subset of them possess large annotated corpora for named entity …

Widespread use of social media during crises has become commonplace, as shown by the
volume of messages during the Haiti earthquake of 2010 and Japan tsunami of 2011 …

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER)
dataset for the 11 major Indian languages from two language families. The dataset contains …

Predicting user intent and detecting the corresponding slots from text are two key problems
in Natural Language Understanding (NLU). In the context of zero-shot learning, this task is …

We present the CSF-Common Semantic Features method for metaphor detection. This
method has two distinguishing characteristics: it is cross-lingual and it does not rely on the …

Transliteration is very common on social media, but transliterated text is not adequately
handled by modern neural models for various NLP tasks. In this work, we combine data …

As developers of a highly multilingual named entity recognition (NER) system, we face an
evaluation resource bottleneck problem: we need evaluation data in many languages, the …

This article describes our efforts to provide ASR resources for Swahili, a Bantu language
spoken in a wide area of East Africa. We start with an introduction on the language situation …