Centering theory and its direct applications Lecture 2.
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Transcript of Centering theory and its direct applications Lecture 2.
Centering theory and its direct applications
Lecture 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
Centering theory ingredients
Deals with local coherenceWhat happens to the flow from
sentence to sentenceDoes not deal with global structuring
of the text (paragraphs/segments) Defines coherence as an estimate of
the processing load required to “understand” the text
Processing load
Upon hearing a sentence a personCognitive effort to interpret the
expressions in the utteranceIntegrates the meaning of the
utterance with that of the previous sentence
Creates some expectations on what might come next
Example
(1) John met his friend Mary today.
(2) He was surprised to see her.
(3) 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
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. c. He was excited that he could finally buy a piano. d. It was closing just as John arrived.
Interpreting pronouns
a. Terry really goofs sometimes.
b. Yesterday was a beautiful day and he was excited about trying out his new sailboat.
c. He wanted Tony to join him on a sailing expedition.
d. He called him at 6am.
e. He was sick and furious at being woken up so early.
Basic center definitions
Centers of an utteranceSet of entities serving to link that
utterance to the other utterances in the discourse segment that contains it
Not words or phrases themselvesSemantic interpretations of noun
phraes
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
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
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
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
Centering rules
If some element of Cf(Un) is realized as a pronoun in Un+1 then so is Cb(Un+1)
Transitions not equalcontinue > retain > smooth-shift >
rough-shift
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
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
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.
E-rater features
Syntactic variety 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?
Ranking forward looking centers
Subject > Indirect object > Object > Others > Quantified indefinite subjects (people,
everyone) > Arbitrary plural pronominals
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
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?
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
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
Centering variations
Continuity (NOCB=lack of continuity) 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)
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.
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
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
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)
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
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 + 0.5*percentage of the orderings that score the same
Results
NOCB gives best resultsSignificantly better than the other
metrics M.BFP is the second best metric
Adding the local rhetorical relations hurts performance---is this surprising?
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