4 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
Abstractive Headline Generation for Spoken Content by Attentive Recurrent Neural Networks with ASR Error Modeling
Lang-Chi Yu, Hung-yi Lee, Lin-shan Lee
Headline generation for spoken content is important since spoken content is difficult to be shown on the screen and browsed by the user. It is a special type of abstractive summari…
Interactive Spoken Content Retrieval by Deep Reinforcement Learning
Yen-Chen Wu, Tzu-Hsiang Lin, Yang-De Chen +2
User-machine interaction is important for spoken content retrieval. For text content retrieval, the user can easily scan through and select on a list of retrieved item. This is imp…
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Bo-Hsiang Tseng, Sheng-Syun Shen, Hung-Yi Lee +1
Multimedia or spoken content presents more attractive information than plain text content, but it's more difficult to display on a screen and be selected by a user. As a result, ac…
Hierarchical Attention Model for Improved Machine Comprehension of Spoken Content
Wei Fang, Jui-Yang Hsu, Hung-yi Lee +1
Multimedia or spoken content presents more attractive information than plain text content, but the former is more difficult to display on a screen and be selected by a user. As a r…