Papers with hierarchical attention mechanism
S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information (D18-1)
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| Challenge: | Existing work on how to generate relevant and informative responses is focusing on how dialogue systems generate information from large dialogue corpus. |
| Approach: | They propose to use dialogue corpus to generate relevant responses by using prototypes to extract semantic information from PMN. |
| Outcome: | The proposed model outperforms classical and strong baseline models in generating relevant and informative responses. |
Claim-Centric and Sentiment Guided Graph Attention Network for Rumour Detection (2024.lrec-main)
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| Challenge: | Social media exerts a substantial influence on individuals' day-to-day existence, a new study shows . the rapid propagation of false information and fake news is a critical aspect of rumour detection . |
| Approach: | They propose a model that takes into account the claim in the source tweet and includes tweet sentiment along with the propagation graph. |
| Outcome: | The proposed model outperforms existing models and improves on sentiment labels. |
Multi-Source Syntactic Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to integrate source syntax into neural machine translations use linearized parses. |
| Approach: | They propose a linearized parsed neural machine translation technique that integrates source syntax into neural machine learning. |
| Outcome: | The proposed model improves over seq2seq and parsed baselines by over 1 BLEU on the WMT17 English-German task. |
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks (2021.findings-emnlp)
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| Challenge: | In computational notebooks, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. |
| Approach: | They propose a new task of code documentation generation for computational notebooks that uses hierarchical attention mechanism to consider code cells and code tokens information when generating documentation. |
| Outcome: | The proposed model outperforms baseline models on a corpus constructed from well-documented Kaggle notebooks. |