Papers with Tree-LSTMs
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness (P18-1)
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| Challenge: | Recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT) has prompted attention mechanisms to be used in machine translation. |
| Approach: | They propose a tree-structured attention model on Tree Long Short-Term Memory Networks . they also experiment with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTS, and Dependency Tree LSTS. |
| Outcome: | The proposed model is based on tree-LSTMs, constituency trees, and dependencies trees. |
Recursive Neural Networks with Bottlenecks Diagnose (Non-)Compositionality (2022.findings-emnlp)
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| Challenge: | Compositional generalisation is often investigated with artificial languages or highly-structured natural language data. |
| Approach: | They propose to use recursive neural models with bottlenecks to generalise compositionally for artificial languages. |
| Outcome: | The proposed model can generalise compositionally for natural language tasks without limiting the transfer of information between nodes. |
Tree Communication Models for Sentiment Analysis (P19-1)
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| Challenge: | Existing methods for sentiment classification over hierarchical phrases capture only bottom-up dependencies between constituents. |
| Approach: | They propose a tree-based sentiment analysis model using graph convolutional neural network and graph recurrent neural network which allows rich information exchange between phrases constituent tree. |
| Outcome: | The proposed model outperforms existing tree-LSTMs in accuracy and efficiency, providing more consistent predictions on phrase-level sentiments. |