Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis (D19-1)
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| Challenge: | Existing deep neural network models such as LSTM and tree-LSTM have a bias problem where the words in the tail of a sentence are more heavily emphasized than those in the header. |
| Approach: | They propose a capsule tree-LSTM model that uses dynamic routing to build sentence representations by assigning different weights to nodes according to their contributions to prediction. |
| Outcome: | The proposed model improves on the Stanford Sentiment Treebank and EmoBank datasets. |
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| Challenge: | Existing methods for sentiment classification over hierarchical phrases capture only bottom-up dependencies between constituents. |
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
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Learning Sentence Representations over Tree Structures for Target-Dependent Classification (N18-1)
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| Challenge: | End-to-end deep learning systems lack flexibility as one cannot adjust the network to fix an obvious problem. |
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A Deep Neural Network Sentence Level Classification Method with Context Information (D18-1)
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| Challenge: | Existing methods that use context for sentence classification are difficult to scale . Usually, sentences are treated as separate instances for the task . however, in many situations the sentence that is the focus of classification appears in a context that can provide additional information. |
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Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
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Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
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Sentence-State LSTM for Text Representation (P18-1)
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| Challenge: | LSTMs have been shown to suffer from various limitations due to their sequential nature. |
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