Fig 3 - uploaded by Devendra Chaplot
Content may be subject to copyright.
Architecture of the Long Short-Term Memory (LSTM) network used for learning representations for the Article selection tutor. The pre-output layer (Fully Connected Layer) is used as the learned representation for each input sentence.
Source publication
A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of scientific interest, but are also valuable in adaptive online tutoring systems. A more accurate model yields more effective tutoring through better instructional decisions....
Contexts in source publication
Context 1
... architecture used for learning representations for Article selection is shown in Figure 3. The input question is split into two parts around the blank. Each character in both the parts has a 32-dimensional embedding. The part before the blank is fed into the forward part of the LSTM sequentially, while the part after the blank is fed into the backward LSTM in the reverse order. At the end of the sequence, both LSTM parts are flattened and combined to a layer of 256 neurons. This layer is fully connected to a pre-output layer with 50 neurons. This layer will serve as the representation for the given input question, which is fully connected to the output layer. The network is trained with all the questions in the English IWT dataset described in Section 2 using stochastic gradient descent with a batch size of 32. At the end of the training, for each input question, the pre-output layer embedding is stored as the feature representation of the ...
Context 2
... architecture used for learning representations for Article selection is shown in Figure 3. The input question is split into two parts around the blank. Each character in both the parts has a 32-dimensional embedding. The part before the blank is fed into the forward part of the LSTM sequentially, while the part after the blank is fed into the backward LSTM in the reverse order. At the end of the sequence, both LSTM parts are flattened and combined to a layer of 256 neurons. This layer is fully connected to a pre-output layer with 50 neurons. This layer will serve as the representation for the given input question, which is fully connected to the output layer. The network is trained with all the questions in the English IWT dataset described in Section 2 using stochastic gradient descent with a batch size of 32. At the end of the training, for each input question, the pre-output layer embedding is stored as the feature representation of the ...
Similar publications
Purpose
The evaluation of spelling performance in aphasia reveals deficits in written language and can facilitate the design of targeted writing treatments. Nevertheless, manual scoring of spelling performance is time-consuming, laborious, and error prone. We propose a novel method based on the use of distance metrics to automatically score spelli...
