Papers with tree-LSTM
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference (C18-1)
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| Challenge: | Existing approaches to perform natural language inference ignore syntactic dependency among words or use tree-LSTM to generate sentence representation with irrelevant information. |
| Approach: | They propose to perform natural language inference with Word-Pair-Dependency-Triplets . they propose to compare the triplets of a given passage-pair to make judgement more interpretable . |
| Outcome: | The proposed approach is better than most of the approaches that use tree structures and comparable to other state-of-the-art approaches. |
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. |