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Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
Published on Nov 27, 201981 Views
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Chapter list
Untitled00:00
Question Answering is Hard!00:00
QA as NL to SPARQL00:24
General approach00:46
Approach00:57
Overview01:00
Overview01:05
Overview01:19
Overview01:26
Overview01:36
How to represent SPARQL01:46
How to represent SPARQL01:55
How to represent SPARQL02:34
Core chain02:44
Core chain02:56
Core chain - dropping entities03:04
Approach03:20
Ranking Framework03:22
Ranking Framework03:48
Ranking Framework03:53
Ranking Framework - Training04:10
Ranking Framework - Training04:25
Ranking Framework - Prediction04:43
Additional Constraints05:08
Approach05:53
Ranking Model06:00
BiLSTM Encoder06:16
BiLSTM Encoder Problems06:31
Solutions06:57
BiLSTM Encoder07:29
Updated BiLSTM Encoder07:30
Slot - Matching Encoder07:45
Slot - Matching Encoder07:57
Slot attention08:06
Experiments08:41
Datasets08:43
Experiments08:53
Baselines08:59
Results09:30
Slot matching analysis10:04
Experiments10:24
Transfer Learning10:28
Transfer between datasets10:50
Transfer between datasets11:00
Pre Training on Language Model11:07
BiLSTM11:16
Pre Training11:32
Pre trained Language Model11:42
Conclusion12:04
Conlusion12:06