no code implementations • 12 Aug 2019 • Federico Marinelli, Alessandra Cervone, Giuliano Tortoreto, Evgeny A. Stepanov, Giuseppe Di Fabbrizio, Giuseppe Riccardi
Natural Language Understanding (NLU) models are typically trained in a supervised learning framework.
no code implementations • EACL 2017 • Y Xia, i, Aaron Levine, Pradipto Das, Giuseppe Di Fabbrizio, Keiji Shinzato, Ankur Datta
We propose a variant of Convolutional Neural Network (CNN) models, the Attention CNN (ACNN); for large-scale categorization of millions of Japanese items into thirty-five product categories.
no code implementations • EACL 2017 • Pradipto Das, Y Xia, i, Aaron Levine, Giuseppe Di Fabbrizio, Ankur Datta
The cataloging of product listings through taxonomy categorization is a fundamental problem for any e-commerce marketplace, with applications ranging from personalized search recommendations to query understanding.
no code implementations • LREC 2012 • Alistair Conkie, Thomas Okken, Yeon-Jun Kim, Giuseppe Di Fabbrizio
The AT{\&}T VoiceBuilder provides a new tool to researchers and practitioners who want to have their voices synthesized by a high-quality commercial-grade text-to-speech system without the need to install, configure, or manage speech processing software and equipment. It is implemented as a web service on the AT{\&}T Speech Mashup Portal. The system records and validates users' utterances, processes them to build a synthetic voice and provides a web service API to make the voice available to real-time applications through a scalable cloud-based processing platform.