Language is often regarded as the hallmark of human intelligence. Developing systems that
can understand human language is thus one of the main obstacles on the quest towards …

While syntactic dependency annotations concentrate on the surface or functional structure of
a sentence, semantic dependency annotations aim to capture between-word relationships …

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

One common characteristic of research works focused on fairness evaluation (in machine
learning) is that they call for some form of parity (equality) either in treatment—meaning they …

In practice, most spoken language understanding systems process user input in a pipelined
manner; first domain is predicted, then intent and semantic slots are inferred according to the …

In the past decade, goal-oriented spoken dialogue systems have been the most prominent
component in today's virtual personal assistants. The classic dialogue systems have rather …

This paper presents the design of the machine learning architecture that underlies the Alexa
Skills Kit (ASK) a large scale Spoken Language Understanding (SLU) Software …

Conversational agents such as Alexa and Google Assistant constantly need to increase their
language understanding capabilities by adding new domains. A massive amount of labeled …

The creation of large-scale open domain reading comprehension data sets in recent years
has enabled the development of end-to-end neural comprehension models with promising …

We tackle the task of building supervised event trigger identification models which can
generalize better across domains. Our work leverages the adversarial domain adaptation …