These concerns all demonstrate the need for a distinctly speech tailored interactive system to help users understand and navigate the spoken language domain.
Specifically, in the context of long-form speech translation systems, where the input transcripts come from Automatic Speech Recognition (ASR), the NMT models have to handle errors including phoneme substitutions, grammatical structure, and sentence boundaries, all of which pose challenges to NMT robustness.
no code implementations • 21 May 2020 • R. Daniel Meyer, Bohdana Ratitch, Marcel Wolbers, Olga Marchenko, Hui Quan, Daniel Li, Chrissie Fletcher, Xin Li, David Wright, Yue Shentu, Stefan Englert, Wei Shen, Jyotirmoy Dey, Thomas Liu, Ming Zhou, Norman Bohidar, Peng-Liang Zhao, Michael Hale
The COVID-19 pandemic has had and continues to have major impacts on planned and ongoing clinical trials.
Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses.
We present Adaptive Memory Networks (AMN) that processes input-question pairs to dynamically construct a network architecture optimized for lower inference times for Question Answering (QA) tasks.