1. Reading Report: Open QA Systems
瞿裕忠
南京大学计算机系

2. Articles
Anthony Fader, Luke Zettlemoyer, Oren Etzioni: Open question answering over curated and extracted knowledge bases. KDD 2014:
Anthony Fader, Luke S. Zettlemoyer, Oren Etzioni: Paraphrase-Driven Learning for Open Question Answering. ACL (1) 2013:

3. Paraphrase-Driven Learning for Open QA
Introduction
The problem of answering questions with the noisy knowledge bases that IE systems produce.
An approach for learning to map questions to formal queries over a large, open-domain database of extracted facts
Learn from a large, noisy, question paraphrase corpus, where question clusters have a common but unknown query
WikiAnswers corpus: syntactic and lexical variations
Learning to answer a broad class of factual questions.

4. Paraphrase-Driven Learning for Open QA
authored(milne,winnie-the-pooh)
treat(bloody-mary, hangover-symptoms)

5. Paraphrase-Driven Learning for Open QA
Lexicon: mappings from NL to DB concepts
The lexicon is used to generate a derivation y from an input question x to a database query z.

6. Paraphrase-Driven Learning for Open QA
A derivation of x under L0
New Lexicon Entry
Word alignment in (x, x’)

7. An initial seed lexicon L0
16 hand-written 2-argument question patterns

8. Compare the following systems
Experiment
Compare the following systems
PARALEX: the lexical learning + parameter learning
NoParam: PARALEX without the learned parameters.
InitOnly: PARALEX using only the initial
Test data
698 questions from WikiAnswers, Create 37 clusters .
A gold standard set of approximately (x, a, l) triples.
Database (noisy KB)
15 million REVERB extractions
The full set of REVERB extractions: over six billion triples
word-alignment for each paraphrase pair
MGIZA++ implementation of IBM Model 4

9. Results

10. Question patterns that are used to derive a correct query
Results
Question patterns that are used to derive a correct query

11. Relation and entity synonyms learned from the WikiAnswers
Results
Relation and entity synonyms learned from the WikiAnswers

12. Error Analysis
How long does it take to drive from Sacramento to Cancun?
What do cats and dogs have in common?
How do you make axes in minecraft?
When were Bobby Orr’s children born?

13. Open QA Over Curated and Extracted KB

14. Open QA Over Curated and Extracted KB
Techniques
The operators and KB are noisy, so it is possible to construct many different sequences of operations (called derivations)
An inference algorithm for deriving high confidence answers and a hidden-variable structured perceptron algorithm for learning a scoring function from data
Automatically mining paraphrase operators from a question corpus and KB-query rewrite operators from multiple KBs.

15. Open QA Over Curated and Extracted KB
Question Templates

16. Open QA Over Curated and Extracted KB
Mine paraphrase operators from the WikiAnswers
WikiAnswers consists of 23 million question-clusters

17. Open QA Over Curated and Extracted KB
Mine query rewrite rules.
Focus on handling the mismatch between relation words in the question and relation words in the KB

18. Experiments Three question sets
Multiple knowl-edge sources: Open IE, Freebase, and Probase.

19. Experiments

20. Experiments

21. Experiments

22. Related papers
Jonathan Berant, Andrew Chou, Roy Frostig, Percy Liang: Semantic Parsing on Freebase from Question-Answer Pairs. EMNLP 2013:
Jonathan Berant, Percy Liang: Semantic Parsing via Paraphrasing. ACL (1) 2014:
Yushi Wang, Jonathan Berant, Percy Liang. Building a Semantic Parser Overnight. Association for Computational Linguistics (ACL), 2015.
Roy Bar-Haim, Ido Dagan, Jonathan Berant: Knowledge-Based Textual Inference via Parse-Tree Transformations. J. Artif. Intell. Res. (JAIR) 54: 1-57 (2015)

23. 致谢
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