Extracting Simplified Statements for Factual Question Generation Michael Heilman and Noah A. Smith 1

lti

Extracting Simplified Statementsfor Factual Question Generation

Michael Heilman and Noah A. Smith

1

lti

Automatic Factual Question Generation (QG)

Input: textOutput: questions for reading assessment (e.g., for a closed-book quiz)

2

We focus on sentence-level factual questions.We focus on sentence-level factual questions.

lti

…Prime Minister Vladimir V. Putin, the country's paramount leader, cut short a trip to Siberia, returning to Moscow to oversee the federal response. Mr. Putin built his reputation in part on his success at suppressing terrorism, so the attacks could be considered a challenge to his stature….

The ProblemIn complex sentences, facts can be presented with varied and complex linguistic constructions.

3

lti

In complex sentences, facts can be presented with varied and complex linguistic constructions.


The Problem

4

main clause

lti



The Problem

5

main clause appositive

lti



The Problem

6


participial phrase

lti



The Problem

7


participial phraseconjunction of clauses

lti


• Prime Minister Vladimir V. Putin cut short a trip to Siberia.• Prime Minister Vladimir V. Putin was the country's

paramount leader.• Prime Minister Vladimir V. Putin returned to Moscow to

oversee the federal response. • Mr. Putin built his reputation in part on his success at

suppressing terrorism.• The attacks could be considered a challenge to his stature.

The Problem

8

Output:Output:

lti

The Rest of the Talk

Input: complex sentenceOutput: set of simple declarative sentences

Our method:• Uses rules to extract and simplify sentences• Is motivated by linguistic knowledge• Outperformed a sentence compression baseline

9

Easier to convert into questionsEasier to convert into questions

lti

Outline

• Introduction and motivation• Our Approach• Simplification and extraction operations• Evaluation• Conclusions

10

lti

Alternative: Sentence CompressionInput: Complex sentenceOutput: Simpler sentence that conveys the main

point.

Suitable for QG?• Only one output per input• Most methods only delete words

11

Knight & Marcu 2000; Dorr et al. 2003;McDonald 2006; Clarke 2008; Martins & Smith 2009;inter alia

Knight & Marcu 2000; Dorr et al. 2003;McDonald 2006; Clarke 2008; Martins & Smith 2009;inter alia

lti

Our Approach

• We extract and simplify multiple statements from complex sentences.

• We include operations for various syntactic constructions.– encoded with pattern matching rules for trees

12

Similar work: Klebanov et al. 2004Similar work: Klebanov et al. 2004

lti

Example: Extracting from Appositives

13

Input: Putin, the Russian Prime Minister, visited Moscow.

Desired Output: Putin was the Russian Prime Minister.

lti


14

NP

Putin visited

VBD

NP

ROOT

S

,

VP

, ,

, NP

Siberia

NP

the Russian Prime Minister

(mainverb)(appositive)(noun)

lti


15

NP < (NP=noun !$-- NP $+ (/,/ $++ NP|PP=appositive !$CC|CONJP)) >> (ROOT << /^VB.*/=mainverb)

NP

Putin visited

VBD

NP

ROOT

S

,

VP

, ,

, NP

Siberia

NP


(mainverb)(appositive)(noun)

lti


16

NP

Putin visited

VBDNP


lti


17

NP

Putin was

VBDNP


Singular past tense form of beSingular past tense form of be

lti


18

was

VBDNP

Putin

NP


S

ROOT

VP

lti

Implementation

• Representation: phrase structure trees from the Stanford Parser

• Syntactic rules are written in the Tregex tree searching language– Tregex operators encode tree relations such as

dominance, sisterhood, etc.

19

Klein & Manning 2003Klein & Manning 2003

Levy & Andrew 2006Levy & Andrew 2006

lti

Outline


20

lti

Encoding Linguistic Knowledge

Given an input sentence A that is assumed true, we aim to extract sentences B that are also true.

Our operations are informed by two phenomena:• semantic entailment • presupposition

21

lti

Semantic Entailment

A entails B:B is true whenever A is true.

22

Levinson 1983Levinson 1983

lti

A: However, Jefferson did not believe the Embargo Act, which restricted trade with Europe, would hurt the American economy.

Simplification by Removing Modifiers

23

Entailment holds when removing certain types of modifiers.Entailment holds when removing certain types of modifiers.

lti



24


discourse marker non-restrictive relative clause

lti



25

B: Jefferson did not believe the Embargo Act would hurt the American economy.


discourse marker non-restrictive relative clause

lti

Extracting from Conjunctions

26

In most clausal and verbal conjunctions, the individual conjuncts are entailed.In most clausal and verbal conjunctions, the individual conjuncts are entailed.

A: Mr. Putin built his reputation in part on his success at suppressing terrorism, so the attacks could be considered a challenge to his stature.

B2: The attacks could be considered a challenge to his stature.

B1: Mr. Putin built his reputation in part on his success at suppressing terrorism.

lti

Extracting from Presuppositions

In some constructions, B is true regardless of whether the main clause of sentence A is true.•i.e., B is presupposed to be true.

In some constructions, B is true regardless of whether the main clause of sentence A is true.•i.e., B is presupposed to be true.

27

Levinson 1983Levinson 1983

A: Hamilton did not like Jefferson, the third U.S. President.

B: Jefferson was the third U.S. President.

negation of main clausenegation of main clause

lti

Presupposition TriggersMany presuppositions have clear syntactic or lexical associations.

28

Trigger Examplenon-restrictive appositives Jefferson, the third U.S. President,

… non-restrictive relative clauses

Jefferson, who was the third U.S. President…

participial modifiers Jefferson, being the third U.S. President, …

temporal subordinate clauses

Before Jefferson was the third U.S. President, …

Jefferson was the third U.S. President.Jefferson was the third U.S. President.

lti

(Over)simplified PseudocodeTake as input a tree t.Extract a set of declarative sentence trees Textracted from

constructions in t.

For each t’ in Textracted :Simplify t’ by removing modifiers.Extract trees Tconjuncts from conjunctions in t’.

For each tconjunct in Tconjuncts :Tresult = Tresult {tconjunct}

Return Tresult

29

by entailment

by entailment

primarily by presuppositionprimarily by presupposition

lti

Outline


30

lti

Baselines• HedgeTrimmer– A rule-based sentence compression algorithm– Iteratively performs simplifying operations until

the input is less than a specified length (15 here).

• “Main clause only”– Only the simplified main clause extracted by the

full system.

31

Dorr et al. 2003Dorr et al. 2003

Both baselines produce one output per input.Both baselines produce one output per input.

lti

Research Questions1. How long are the simplified outputs and how

many are there?– Extracted statements from 25 previously unseen

Encyclopedia Britannica articles about cities.

2. How well do the extracted statements cover the information in the input texts?– % of input words in at least one output.

32

Barzilay & Elhadad 2003Barzilay & Elhadad 2003

lti

Results: Length & Coverage

33

Input Texts

Hedge-Trimmer

Main clause only

Full

Sentences per input

1.00 1.00 1.00 1.63

Sentence length

23.5 12.4 15.4 13.4

Word coverage (%)

100.0 61.4 69.1 87.9

lti

Research Questions

3. How well does our system preserve fluency and correctness?– Two raters judged simplified outputs for fluency

and correctness using 1-5 scales.– We averaged the raters’ scores.

34

Inter-rater agreement: r = .92 for fluencyr = .82 for correctness

Inter-rater agreement: r = .92 for fluencyr = .82 for correctness

lti

Results: Fluency & Correctness

Input Texts

Hedge-Trimmer

Main clause only

Full

Fluency - 3.53 4.72 4.75Correctness - 3.75 4.71 4.67Rated 5 for both (%)

- 44.0 78.7 75.0

35

Differences between HedgeTrimmer and Full are statistically significant (p < .05).Differences between HedgeTrimmer and Full are statistically significant (p < .05).

lti

Outline


36

lti

Conclusions

• Method for extracting simplified declarative statements from complex sentences.

• Outperformed a text compression baseline.– More outputs and better coverage– Higher % of fluent and correct outputs

• Future work: evaluation of this as a component in a QG system.

37

Heilman & Smith 2010Heilman & Smith 2010

lti

Questions?

38

Demo & code release available on my website.http://www.cs.cmu.edu/~mheilman

Demo & code release available on my website.http://www.cs.cmu.edu/~mheilman

lti

A Whale of a Sentence

“As they narrated to each other their unholy adventures, their tales of terror told in words of mirth; as their uncivilized laughter forked upwards out of them, like the flames from the furnace; as to and fro, in their front, the harpooneers wildly gesticulated with their huge pronged forks and dippers; as the wind howled on, and the sea leaped, and the ship groaned and dived, and yet steadfastly shot her red hell further and further into the blackness of the sea and the night, and scornfully champed the white bone in her mouth, and viciously spat round her on all sides; then the rushing Pequod, freighted with savages, and laden with fire, and burning a corpse, and plunging into that blackness of darkness, seemed the material counterpart of her monomaniac commander's soul.”

39

Melville 1851Melville 1851

Gold standard parse:Gold standard parse:

133 word sentence from Moby Dick:133 word sentence from Moby Dick:

lti

A Whale of a Sentence

40

1. The rushing Pequod seemed the material counterpart of her monomaniac commander's soul.2. They narrated to each other their unholy adventures.3. Their uncivilized laughter forked upwards out of them.4. The harpooneers wildly gesticulated with their huge pronged forks and dippers in their front.5. The wind howled on.6. The sea leaped.7. The ship groaned.8. The ship dived.9. The ship steadfastly shot her red hell further and further into the blackness of the sea and the night.10. The ship scornfully champed the white bone in her mouth.11. The ship viciously spat round her on all sides.12. The rushing Pequod was freighted with savages.13. The rushing Pequod was laden with fire.14. The rushing Pequod was burning a corpse.15. The rushing Pequod was plunging into that blackness of darkness.16. Their unholy adventures were their tales of terror told in words of mirth.

System output:System output:

Documents

Extracting Simplified Statements for Factual Question Generation Michael Heilman and Noah A. Smith 1