GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering thumbnail
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GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering

Published on Jun 13, 202311 Views

Chapter list

GETT-QA: Graph Embedding based Text-to-Text Transformer for Knowledge Graph Question Answering00:00
KGQA00:18
Motivation00:56
Model First Sketch01:46
Model First Sketch 102:39
Model First Sketch 203:12
Model First Sketch 303:39
Model Potential solution04:03
Model Second Sketch05:12
Model Second Sketch 105:31
Model Second Sketch 206:12
Model Second Sketch 306:16
Model Second Sketch 406:45
Truncated Embedding06:52
Nature of Learning07:56
Truncated Embedding08:58
Candidate Ordering09:15
Candidate Ordering 110:06
Candidate Ordering 210:21
Evaluation Datasets10:51
Evaluation Results11:04
Evaluation Results 111:31
Re-Ordering Samples11:58
Re-Ordering Samples 112:16
Re-Ordering Samples 212:30
Limitations12:38
Highlights13:00
Questions13:15