2 research outputs found
Self-Attention Gazetteer Embeddings for Named-Entity Recognition
Recent attempts to ingest external knowledge into neural models for
named-entity recognition (NER) have exhibited mixed results. In this work, we
present GazSelfAttn, a novel gazetteer embedding approach that uses
self-attention and match span encoding to build enhanced gazetteer embeddings.
In addition, we demonstrate how to build gazetteer resources from the open
source Wikidata knowledge base. Evaluations on CoNLL-03 and Ontonotes 5
datasets, show F1 improvements over baseline model from 92.34 to 92.86 and
89.11 to 89.32 respectively, achieving performance comparable to large
state-of-the-art models.Comment: Preprin
Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
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 Development Kit (SDK) that enables developers to
extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the
infrastructure powers over 25,000 skills deployed through the ASK, as well as
AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability
and a rapid iteration cycle for third party developers. It imposes inductive
biases that allow it to learn robust SLU models from extremely small and sparse
datasets and, in doing so, removes significant barriers to entry for software
developers and dialogue systems researchers.Comment: Published at the 1st Workshop on Conversational AI at NIPS 2017
(NIPS-WCAI