1. Centering theory and its direct applications
Lecture 2

2. Some definitions Discourse = coherent sequence of utterances
Several sentences following one another do not make a readable text
Defining specific computable measures of coherence is the goal of this seminar

3. Centering theory ingredients
Deals with local coherence
What happens to the flow from sentence to sentence
Does not deal with global structuring of the text (paragraphs/segments)
Defines coherence as an estimate of the processing load required to “understand” the text

4. Processing load Upon hearing a sentence a person
Cognitive effort to interpret the expressions in the utterance
Integrates the meaning of the utterance with that of the previous sentence
Creates some expectations on what might come next

5. Example John met his friend Mary today. He was surprised to see her.
He thought she is still in Italy.
Form of referring expressions
Anaphora needs to be resolved
“Create” a discourse entity at first mention with full noun phrase
Creating expectations

6. Creating and meeting expectations
(1) a. John went to his favorite music store to buy a piano.
b. He had frequented the store for many years.
c. He was excited that he could finally buy a piano.
d. He arrived just as the store was closing for the day.
(2) a. John went to his favorite music store to buy a piano.
b. It was a store John had frequented for many years.
d. It was closing just as John arrived.

7. Interpreting pronouns
Terry really goofs sometimes.
Yesterday was a beautiful day and he was excited about trying out his new sailboat.
He wanted Tony to join him on a sailing expedition.
He called him at 6am.
He was sick and furious at being woken up so early.

8. Basic center definitions
Centers of an utterance
Set of entities serving to link that utterance to the other utterances in the discourse segment that contains it
Not words or phrases themselves
Semantic interpretations of noun phraes

9. Types of centers Forward looking centers
An ordered set of entities
What could we expect to hear about next
Ordered by salience as determined by grammatical function
Subject > Indirect object > Object > Others
John gave the textbook to Mary.
Cf = {John, Mary, textbook}
Preferred center Cp
The highest ranked forward looking center
High expectation that the next utterance in the segment will be about Cp

10. Backward looking center
Single backward looking center, Cb (U)
For each utterance other than the segment-initial one
The backward looking center of utterance Un+1 connects with one of the forward looking centers of Un
Cb (U+1) is the most highly ranked element from Cf (Un) that is also realized in U+1

11. Centering transitions ordering
Cb(Un+1)=Cb(Un) OR
Cb(Un)=[?]
Cb(Un+1) != Cb(Un)
Cb(Un+1) = Cp (Un+1)
continue
smooth-shift
Cb(Un+1) != Cp (Un+1)
retain
rough-shift

12. Centering constraints
There is precisely one backward-looking center Cb(Un)
Cb(Un+1) is the highest-ranked element of Cf(Un) that is realized in Un+1

13. Centering rules
If some element of Cf(Un) is realized as a pronoun in Un+1 then so is Cb(Un+1)
Transitions not equal
continue > retain > smooth-shift > rough-shift

14. Centering analysis Terry really goofs sometimes.
Cf={Terry}, Cb=?, undef
Yesterday was a beautiful day and he was excited about trying out his new sailboat.
Cf={Terry,sailboat}, Cb=Terry, continue
He wanted Tony to join him in a sailing expedition.
Cf={Terry, Tony, expedition}, Cb=Terry, continue
He called him at 6am.
Cf={Terry,Tony}, Cb=Terry, continue

15. Tony was sick and furious at being woken up so early.
He called him at 6am.
Cf={Terry,Tony}, Cb=Terry, continue
Tony was sick and furious at being woken up so early.
Cf={Tony}, Cb=Tony, smooth shift
He told Terry to get lost and hung up.
Cf={Tony,Terry}, Cb=Tony, continue
Of course, Terry hadn’t intended to upset Tony.
Cf={Terry,Tony}, Cb = Tony, retain

16. Rough shifts in evaluation of writing skills
One of the graders of student essays in standardized tests is an automatic program
ETS researchers have developed a number of applications that use natural language processing technologies to evaluate and score the writing abilities of test takers:
The CriterionSM Online Essay Evaluation Service automatically evaluates essay responses using e-rater and the Critique writing analysis tools.
E-rater® gives holistic scores for essays.
CritiqueTM provides real-time feedback about grammar, usage, mechanics and style, and organization and development.
C-raterTM offers automated analysis of conceptual information in short-answer, free responses.

17. E-rater features Syntactic variety Clear transitions
Represented by features that quantify the occurrence of clause types
Clear transitions
Cue phrases in certain syntactic constructions
Existence of main and supporting points
Appropriateness of the vocabulary content of the essay
What about local coherence?

18. Ranking forward looking centers
Subject >
Indirect object >
Object >
Others >
Quantified indefinite subjects (people, everyone) >
Arbitrary plural pronominals

19. Essay score model Human score available E-rater prediction available
Percentage of rough-shifts in each essay: analysis done manually
Negative correlation between the human score and the percentage of rough-shifts

20. Karamanis’07 Why are we reading this paper?
Gives quite complete list of references on later work on centering
Centering variants
Reminds that entity coherence is not the only factor in text flow
We’ll be discussing rhetorical structure theory during the next class
Applications---can some aspects of the work be done differently/improved upon?

21. Information ordering task
Given a set of sentences/clauses, what is the best presentation?
Take a newspaper article and jumble the sentences---the result will be much more difficult to read than the original
Criteria for deciding which of two orderings is better
Centering would definitely be applicable
Summarization, question answering, generation

22. Linear multi-factor regression
Approximate the human score as a linear function of the e-rater prediction and the percentage of rough-shifts
Adding rough shifts significantly improves the model of the score
0.5 improvement on 1—6 scale
How easy/difficult would it be to fully automate the rough-shift variable

23. Centering variations Continuity (NOCB=lack of continuity) Coherence
Cf(Un) and Cf(Un+1) share at least one element
Coherence
Cb(Un) = Cb(Un+1)
Salience
The Cb(U) = Cp(U)
Coherence is more important than salience
Cheapness (fulfilled expectations)
Cb (Un+1) = Cp(Un)

24. GNOME corpus 20 descriptions of museum artifacts
Split into finite unites (clauses)
Semi-automatic centering annotation
Item 144 is a torc. Its present arrangement, twisted into three rings, may be a modern alteration; it should probably be a single ring, worn around the neck. The terminals are in the form of goats’ heads.

25. Rhetorical coherence
Each text can be seen as a hierarchical tree structure
Different spans are related by some rhetorical relation
Elaboration (adding more information)
Contrast
Sequence
Purpose
Summary etc

26. Local rhetorical coherence
Applies only locally rather than on the text as a whole
Signaled by cue phrases
Contrast: but, however, on the other hand
Continuation: and, then, later
Consequence: because, in order to, so
These local rhetorical relations structure the text
When missing, entity coherence determines the flow
8 out of the 20 texts do not have any explicitly marked rhetorical relations

27. Joint centering and local rhetorical coherence
In clauses directly marked for a rhetorical relation
Merge the Cf lists of the two clauses
Apply centering transitions on the resulting Cf list rather than the original
GNOME-RR contains 1.58 fewer CF lists compared to the original average number (8.35)

28. Metrics of coherence M.NOCB (no continuity)
M.CHEAP (expectations not met)
M.KP sum of the violations of continuity, cheapness, coherence and salience
M. BFP seeks to maximize transitions according to Rule 2

29. Experimental methodology
Gold-standard ordering
The original order of the text (object description, news article)
Assume that other orderings are inferior
Classification error rate
Percentage orderings that score better than the gold-standard *percentage of the orderings that score the same

30. Results NOCB gives best results M.BFP is the second best metric
Significantly better than the other metrics
M.BFP is the second best metric
Adding the local rhetorical relations hurts performance---is this surprising?

31. Reminders Select topics you would like to present
Should schedule next week now
The second time you present one of the goals will be to relate the papers with previous topics we have covered
Start thinking about the topic of your literature overview
About 15 papers
5/6 pages
Due Nov 12