Interactive Spoken Content Retrieval with Deep Reinforcement Learning
碩士 === 國立臺灣大學 === 電信工程學研究所 === 104 === Interactive retrieval is important for spoken content. The reason is because when looking for text documents, one can easily scan through and select on a search engine result page, whereas similar privileges don not exist when searching for spoken content. Besi...
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ndltd-TW-104NTU054350772017-05-07T04:26:42Z http://ndltd.ncl.edu.tw/handle/37478361307949330114 Interactive Spoken Content Retrieval with Deep Reinforcement Learning 使用深層強化學習之互動式語音數位內容檢索 Yen-Chen Wu 吳彥諶 碩士 國立臺灣大學 電信工程學研究所 104 Interactive retrieval is important for spoken content. The reason is because when looking for text documents, one can easily scan through and select on a search engine result page, whereas similar privileges don not exist when searching for spoken content. Besides, it is hard for the users to find the desired spoken content when the search results are noisy, which usually happens due to the imperfect speech recognition components in spoken content retrieval. A way to counter the difficulties of spoken content retrieval is human-machine interaction that machine takes different actions to request additional information from the user to obtain better retrieval results. The most suitable actions depend on the situations, so in previous works, some hand-crafted states estimated from the current search results are used to determine the actions, but the hand-crafted states are not necessary the best indicator for choosing actions. In this paper, we applied the Deep-Q- Learning method in interactive retrieval of spoken content. Deep-Q- Learning sidesteps the estimation of the hand-crafted states and can directly determine the action based on retrieval results without any human knowledge. It reached discernible improvements compared with the hand-crafted states. 李琳山 2016 學位論文 ; thesis 80 zh-TW |
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碩士 === 國立臺灣大學 === 電信工程學研究所 === 104 === Interactive retrieval is important for spoken content. The reason is because when looking for text documents, one can easily scan through and select on a search engine result page, whereas similar privileges don not exist when searching for spoken content. Besides, it is hard for the users to find the desired spoken content when the search results are noisy, which usually happens due to the imperfect speech recognition components in spoken content retrieval. A way to counter the difficulties of spoken content retrieval is human-machine interaction that machine takes different actions to request additional information from the user to obtain better retrieval results. The most suitable actions depend on the situations, so in previous works, some hand-crafted states estimated from the current search results are used to determine the actions, but the hand-crafted states are not necessary the best indicator for choosing actions. In this paper, we applied the Deep-Q- Learning method in interactive retrieval of spoken content. Deep-Q- Learning sidesteps the estimation of the hand-crafted states and can directly determine the action based on retrieval results without any human knowledge. It reached discernible improvements compared with the hand-crafted states.
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李琳山 |
author_facet |
李琳山 Yen-Chen Wu 吳彥諶 |
author |
Yen-Chen Wu 吳彥諶 |
spellingShingle |
Yen-Chen Wu 吳彥諶 Interactive Spoken Content Retrieval with Deep Reinforcement Learning |
author_sort |
Yen-Chen Wu |
title |
Interactive Spoken Content Retrieval with Deep Reinforcement Learning |
title_short |
Interactive Spoken Content Retrieval with Deep Reinforcement Learning |
title_full |
Interactive Spoken Content Retrieval with Deep Reinforcement Learning |
title_fullStr |
Interactive Spoken Content Retrieval with Deep Reinforcement Learning |
title_full_unstemmed |
Interactive Spoken Content Retrieval with Deep Reinforcement Learning |
title_sort |
interactive spoken content retrieval with deep reinforcement learning |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/37478361307949330114 |
work_keys_str_mv |
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