1. ComplQA: Complex Question Answering over Knowledge Base
Yawei Sun, Lingling Zhang, Gong Cheng, and Yuzhong Qu

2. Outline
Introduction
Approach
Experiments
Conclusion

3. Introduction Simple Question Complex Question Simple Relation
e.g., Who is the wife of Barack Obama ?
Complex Question
Multiple Relations
e.g., What type of music featured on the album Epica was composed by Mozart ?

4. Introduction - existing works and challenge
Siva: transform dependency tree to logical form. (emnlp17)
Abu: recognize sub-questions from dependency tree. (www17)
Zou: construct query graph based on dependency tree. (tkde18)
Challenge 1: parser error problem
Question parser error leads to their ill-formed representations.

5. Introduction - existing works and challenge
Cheng: structurally isomorphic.(acl17)
Siva: not isomorphic, two operations: contract, and expand. (emnlp17)
Zou: not isomorphic, spanning tree of super graph. (tkde18)
Challenge 2: structure mismatch problem

6. Introduction - our solution
To alleviate structure mismatch problem
Expand existing work by ‘Insert Node’ operation.
To alleviate parser error problem
Propose Span Tree
What type was composed ?
by Mozart
of music
featured on the album Epica

7. Approach - pipeline (1) Parsing (2) Grounding Question 1.1 Span Tree
Grounded Graph
Question
Ungrounded Graph
1.1 Span Tree
2.1 Node Linking
1.2 Graph Generation
2.2 Structure Mapping

8. Example: What type of music featured on the album Epica was composed by Mozart ?
Class: what type of music
?music.genre 1 1
featured album
composed
music.album.genre
music.artist.genre 2 3 2 3
Entity: Epica
Entity: Mozart
m.010rcgyt
m.082db
Ungrounded Graph
Grounded Graph

9. 1. Parsing
p ungrounded graph question)= p ungrounded graph span tree)∗p span tree question)
1.1 Span Tree: p span tree question)
1.2 Graph Generation: p ungrounded graph span tree)

10. 1.1 Span Tree - p span tree question)
Definition: a skeleton with modifier spans (allowed nested)
Algorithm 1. Seq2List:
Input: question; Output: span tree
tokens = [token1, token2,…tokenn]
num_layer_list = [0…0], epoch=0
While not finished do
epoch += 1
Predict redundancy_span
Update(num_layer_list, redundancy_span, epoch)
Shield redundancy_span in tokens
Update(num_layer_list, skeleton, epoch+1)
Return List2Tree(num_layer_list)
Algorithm 2. List2Lree:
Input: num_layer_list, tokens
Output: span tree
span_tree = tree()
While i <= max(num_layer_list) do
span_tree.add(current_span)
span_tree.add(child_span)
hd = find_headword (child_span, current_span)
span_tree.addedge(span,child_span, hd)
Return span_tree

11. 1.1 Span Tree - example
epoch
What
type of
music
featured on
the
album
Epica
was
composed by
Mozart 1 2 3
What type was composed ?
by Mozart
[4, 4, 2, 2, 1, 1, 1, 1, 1, 4, 4, 3, 3, 4]
of music
featured on the album Epica

12. 1.2 Graph Generation p ungrounded graph span tree)
1.2.1 Node Recognition
1.2.2 Relation Extraction
Class: what type of music 1
What type was composed ?
by Mozart
featured album
composed 2 3
of music
featured on the album Epica
Entity: Epica
Entity: Mozart
Ungrounded Graph

13. 2. Grounding p grounded graph ungrounded graph) 2.1 Node Linking
2.2 Structure Mapping

14. 2.1 Node Linking Entity Linking Class Linking
A dictionary-based method
E.g., Mozart -- m.082db
Class Linking
A word-embedding similarity method
E.g,, what type of music -- music.genre
?music.genre 1 2 3
m.010rcgyt
m.082db

15. 2.2 Structure Mapping 2.2.1 Maping 2.2.2 Path Match 2.2.3 Ranking
Method 1: Instance-Level
Method 2: Schema-Level
2.2.2 Path Match
2.2.3 Ranking
Maximum-entropy model
?music.genre 1
music.album.genre
music.artist.genre 2 3
m.010rcgyt
m.082db

16. 2.2.1 Maping Instance-Level Schema-Level ?music.genre ?music.genre
Insert Node Mediator
m.010rcgyt --{}-- ?x
m.082db --{}-- ?x
Schema-Level
Insert Node
m.082db --{}-- ?x 1 2 3
m.010rcgyt
m.082db 2 3
m.010rcgyt
m.082db

17. Path Match

18. Experiments - GraphQuestions
Baselines F1
SEMPRE 13
10.80
PARASEMPRE 14
12.79
JACANA 14
5.08
Siva 17
17.6
Cheng 17
17.02
Dong 17
20.4
ComplQA
26.16

19. Experiments - ComplexWebQuestions
Baselines
Precision
SIMPQA + PRETRAINED
19.9
SPLITQA + PRETRAINED
25.9
MHQA-GRN
30.1
SplitQA + data augmentation
34.2
Human 63
ComplQA

20. Conclusion Parsing Grounding Challenge 1: parser error problem
Solution: Span Tree
Grounding
Challenge 2: structure mismatch problem
Solution: Insert Node

21. Thank you
Q & A