Xiang Hao

In this work, we pose intro and recap detection as a supervised sequence labeling problem and propose a novel end-to-end deep learning framework to this end.

In this work, we develop a data collection pipeline to address long sequence of texts and integrate this pipeline with a multi-head self-attention model.

We show that, (a) audio based approach results in superior performance compared to other baselines, (b) benefit due to audio model is more pronounced on global multi-lingual data compared to English data and (c) the multi-modal model results in 63% rating accuracy and provides the ability to backfill top 90% Stream Weighted Coverage titles in PV catalog with 88% coverage at 91% accuracy.