Papers by Maria Maleshkova
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks (2021.eacl-main)
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| Challenge: | Existing knowledge graphs are widely used for (complex) conversational question answering . LASAGNE improves the F1-score on eight out of ten question types . |
| Approach: | They propose a multi-task neural semantic parsing approach for (complex) conversational question answering over a knowledge graph using a transformer model and a Graph Attention Networks model. |
| Outcome: | The proposed approach outperforms baselines on eight out of ten question types on a standard dataset for complex sequential question answering. |