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Hierarchical Recurrent Encoder-Decoder (HRED) Neural Network Training Algorithm: Difference between revisions
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Hierarchical Recurrent Encoder-Decoder (HRED) Neural Network Training Algorithm (view source)
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|[[File:2015_AHierarchicalRecurrentEncoderDe_Fig3.png|750px]] | |[[File:2015_AHierarchicalRecurrentEncoderDe_Fig3.png|750px]] | ||
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|+ align="bottom" style="caption-side:bottom;text-align:left;font-weight:normal; padding-left: 1em;padding-right: 1em; padding-top:0.2em;padding-bottom:0.2em;background-color:#F4F6F6 "|'''Figure 3:''' The [[hierarchical recurrent encoder-decoder (HRED) for query suggestion]]. </s> Each arrow is a [[non-linear transformation]]. </s> The [[user]] types <code>cleveland gallery</code> → <code>lake erie art</code>. </s> During [[training]], the [[model]] [[encode]]s <code>cleveland gallery</code>, [[update]]s the [[session-level recurrent state]] and [[maximize]] the [[probability]] of seeing the following [[query]] <code>lake erie art</code>. </s> The process is repeated for all [[queri]]es in the [[session]]. </s> During [[testing]], a [[contextual suggestion]] is generated by [[encoding]] the previous [[queri]]es, by updating the [[session-level recurrent state]]s accordingly and by [[sampling]] a new [[query]] from the last obtained [[session-level recurrent state]]. </s> In the example, the generated [[contextual suggestion]] is <code>cleveland indian art</code>. | |+ align="bottom" style="caption-side:bottom;text-align:left;font-weight:normal; padding-left: 1em;padding-right: 1em; padding-top:0.2em;padding-bottom:0.2em;background-color:#F4F6F6 "|'''Figure 3:''' The [[hierarchical recurrent encoder-decoder (HRED) for query suggestion]]. </s> Each arrow is a [[non-linear transformation]]. </s> The [[user]] types <code>cleveland gallery</code> → <code>lake erie art</code>. </s> During [[training]], the [[model]] [[encode]]s <code>cleveland gallery</code>, [[update]]s the [[session-level recurrent state]] and [[maximize]] the [[probability]] of seeing the following [[query]] <code>lake erie art</code>. </s> The process is repeated for all [[queri]]es in the [[session]]. </s> During [[testing]], a [[contextual suggestion]] is generated by [[encoding]] the previous [[queri]]es, by updating the [[session-level recurrent state]]s accordingly and by [[sampling]] a new [[query]] from the last obtained [[session-level recurrent state]]. </s> In the example, the generated [[contextual suggestion]] is <code>cleveland indian art</code>. | ||
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