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CS 544: Lecture 3.5 Discourse Coherence. Jerry R. Hobbs USC/ISI Marina del Rey, CA. Outline. Interpreting Adjacency What Coherence Relations are there? Definitions and Examples of Specific Coherence Relations Discourse Structure. Interpretation. - PowerPoint PPT Presentation
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CS 544: Lecture 3.5Discourse Coherence
Jerry R. HobbsUSC/ISI
Marina del Rey, CA
Outline
Interpreting Adjacency
What Coherence Relations are there?
Definitions and Examples of Specific Coherence Relations
Discourse Structure
Interpretation
To understand our environment, we seek
the best explanation of the observable facts.
To understand a text, we seek the best explanation
of the "observable facts" that the text presents.
Interpreting Adjacency
Adjacency is one of the observable facts to be explained.
Environment: chair on table
Text: Two segments of text x and y together.
turpentine jar R = y's function is to contain x
oil sample R = y is sample of x
Compositional Semanticsas Interpretation of Adjacency
oil sample
R = y is sample of x
men work
R = y is a working event by x
Syntax and compositional semantics are constraints on the interpretation of adjacency as predicate-argument relations.
Discourse Coherence
John can open Bill's safe. He knows the combination.
Interpreting text includes explaining the adjacency of clauses, sentences, and larger segments of discourse.
= Finding relation between adjacent segments
Discourse CoherenceRelation
Segment1 Segment2
Interpret each segment, and find the relation between them.
causefigure-ground and ground-figuresimilarity and contrast
R4
R3
R1 R2
S1 S2 S3 S4 S5
The Structure of Discourse
Back to the Boat
Boat in Tree by Sea
Storm
ExplainEntities in
Environmentcause
Explain Relationsin Environment
“Help! Thief!”
Explain Wordsin Utterance
Explain Relationsbetween Them
(Why are they adjacent?)
Tasks of a Discourse Theory
1. What are the possible relations between adjacent discourse segments?
2. How are they recognized or characterized?
Interpreting Adjacent Sentences
Sentence-1 Sentence-2Relationbetween
Event Event
Possible Relations: Cause Similarity Background .....
Coherence Relations
Outline
Interpreting Adjacency
What Coherence Relations are there?
Definitions and Examples of Specific Coherence Relations
Discourse Structure
Coherence Relations
Causality: Cause, Explanation, Metatalk, ....
Change of State: Occasion
Figure-Ground: Background
Similarity: Parallelism, Contrast, Exemplification
Coarsening of Granularity: Elaboration, ....
Coherence RelationsSentences and larger segments of text describe situations or eventualities.
What are the principal kinds of relations that can obtain between situations/eventualities?
Figure-ground or Ground-figure March Madness is happening. USC won on Sunday. Interlocking change of state (occasion) He drives to the basket. He dunks it. Causality and its violation USC played excellent defense. Texas only scored 68. Texas had the best player. USC won anyway. Similarity and its negation (contrast) UCLA advanced. USC also advanced. UCLA won narrowly. USC won handily. including the limiting case of Elaboration USC tromped Texas. We dominated the game. Predicate-argument Duke lost! Again!
These are semantic relations (the information conveyed by adjacency), not rhetorical relations (what the speaker is trying to do by putting these together)
Functionality of Coherence Relations
Figure-ground or Ground-figure Interlocking change of state Causality and its violation
Similarity and its negation (contrast)
including the limiting case of Elaboration Predicate-argument
The environment influenceswhat happens to an entity in
that environment.
These allow us to predictwhat will happen next.
Similar thingsbehave similarly.
The basic unitof information.
Formalizing the Tree Structureof Discourse
Logical form of sentence s --> Syn(s,e)
Syn(s,e) --> Segment(s,e)
Segment(s1,e1) & Segment(s2,e2) & CoRel(e1,e2,e) --> Segment(s1 s2, e)
Note: Syntactic composition rules are an instance of this rule, where relation is pred-arg.
To interpret text, prove: ( e) Segment(text, e)
Summary
Outline
Interpreting Adjacency
What Coherence Relations are there?
Definitions and Examples of Specific Coherence Relations
Discourse Structure
The Ground-Figure Relation
composite-entity(s) & relations-of(r,s) & member(e1,r) & p'(e1,x,y) & at'(e2,x,y) --> CoRel(e1,e2,e2)
S1 describes some aspect of a composite entity (the ground).S2 places an entity x (the figure) at some point within that system.
March Madness is happening. USC won on Sunday.
T is a pointer to the root of a binary tree.Set the variable P to T.
Change of State: Occasion
change(e,e1,e2) --> CoRel(e1,e,e)
change(e,e1,e2) --> CoRel(e,e2,e)
change(e4,e1,e2) & change(e5,e2,e3) & change(e6,e1,e3) --> CoRel(e4,e5,e6)
John walked to the door. He opened it. He stepped out.
Typically e6 is a higher-level, coarser-grained description of the sequence of changes.
Causality and Explanation
cause(e2,e1) --> CoRel(e1,e2)
A segment of discourse conveying e2 explains a segment conveying e1 if e2 could cause e1.
The police prohibited the women from demonstrating.They feared violence.
Segment1 <- explains - Segment2
e1 <- causes - e2
describes describes
Explanation: Example 1The police prohibited the women from demonstrating. They feared violence.
Logical Form:
prohibit'(p1,p,d) & demonstrate'(d,w) & CoRel(p1,f,p1)
& fear'(f,y,v) & violent'(v,z) cause(f,p1)
Knowledge Base:
fear'(f,p,v) --> diswant'(d2,x,v) & cause(f,d2)
demonstrate'(d,w) --> cause(d,v) & violent'(v,z)
cause(d,v) & diswant'(d2,p,v) --> diswant'(d1,p,d) & cause(d2,d1)
diswant'(d1,p,d) & authority(p) --> prohibit'(p1,p,d) & cause(d1,p1)
cause(e1,e2) & cause(e2,e3) --> cause(e1,e3)
(Winograd)
Causality: Example 2
cause(e2,e1) --> CoRel(e1,e2,e1)
Bush supports big business. He will veto Bill 1711.
Causality: Example 2
Required KnowledgeBush supports big business. He will veto Bill 1711.
KB:
support'(e1,x,y) & bad-for(z,y) --> prevent'(e2,x,z) & cause(e1,e2)
prevent'(e2,x,z) & etc1(e2,x,z) --> veto'(e2,x,z)
Example 2: The InterpretationBush supports big business. He will veto Bill 1711.LF:
support'(e1,Bush,BB)
& CoRel(e1,e2,e) & veto'(e2,x,1711)
cause(e1,e2)
prevent'(e2,x,1711)
x = Bush etc1(e2,x,1711)
bad-for(1711,BB)
e = e2
Causality: Example 3Peter: Do you want to go to the cinema?Mary: I'm tired.
Mary didn't want to go to the cinema. She was tired.
diswant'(e1,M,e2)
cause(e3,e1)
diswant'(e1,M,e2) & activity(e2)
go'(e2,M,c) cinema(c) CoRel(e1,e3) tired'(e3,x)
etc(e2,x)
x=M
x=M
Causality: Example 4Ann: Why are you so happy?Beth: I finally met a guy who is a bachelor.
Beth was so happy. She finally met a guy who was a bachelor.
happy'(e1,B) CoRel(e1,e2) meet'(e2,B,g) guy(g) bachelor(g)
poss'(e3,e5) & marry’(e5,B,g)
meet&date'(e4,B,g) & eligible(g) & bachelor(g)
cause(e4,e3)
cause(e3,e1)
cause(e4,e1)
Explanation: Example 5I don’t own a TV set. I would watch it all the time.
Rexists(e1) own’(e2,i,t) CoRel(e1,e3,e1) Rexists(e3)
not’(e1,e2) tv(t) would’(e3,e4,c) watch’(e4,i,x)
not’(e1,e2) cause(e3,e1)
bad-for(e4,i) cause’(e3,e2,e4)
watch’(e4,i,t)
use’(e4,i,t)tv(t)
own’(e2,i,t)
c=e2
x=t
Owning causes using
To use TV is to watch it
Watching TV is bad
bad effect causes avoid cause
would (given C)if C causes
Explanation andDefinite Reference
restaurant(a) canteen(b) prefer’(p,i,a,b) CoRel(p,e) capp(c) cheaper’(e,c,z)
cause(e,p)
sell(a,c) sell(b,z) capp(z)
I prefer the restaurant on the corner to the student canteen.The cappuccino is less expensive there.
(Matsui)
Restaurantssell cappucino
Canteens sellcappucino
I’m cheap
Coherence Relations Based on Similarity
Specific -> Specific -> General -> Specific General Specific
Positive: Parallel Generalizaton Exemplification (Elaboration)
Negative: Contrast -- --
Question-Answer pairs
SimilarityProperties are similar, if they are or imply properties whose predicates are the same, and whose arguments are coreferential or similar.
Similar[ p’(e1,x1, ..., z1), p’(e2,x2, ..., z2) ] : Coref(x1,...,x2,...) OR Similar(x1,x2) .... Coref(z1,...,z2,...) OR Similar(z1,z2)
Arguments are similar, if their other inferentially independent properties are similar.
Similar[ x1,x2 ] : Similar[ p1(...,x1,...), p2(...,x2,...) ] .... Similar[ q1(...,x1,...), q2(...,x2,...) ]
Mapping is preserved as recursion progresses.
Inferential Independence: K, P =/=> Q; K, Q =/=> P
Similarity: Example
A ladder weighs 100 lb with its center of gravity 20 ft from the foot,and a 150 lb man is 10 ft from the top.
force(w1,L,d1,x1) w1: lb(w1,100) L: ladder(L) d1: Down(d1) x1: distance(x1,f, 20 ft) f: foot(f,L) ==> end(f,L) L:
force(w2,y,d2,x2) w2: lb(w2,150) y: ==> Coref(y,...,L,...) d2: Down(d2) x2: distance(x2,t, 10 ft) t: top(t,z) ==> end(t,z) z: ==> Coref(z,...,L,...)
Complicated to formalize, but easy for brains
Verb Phrase Ellipsis
John revised his paper before the teacher did.
before(e11,e21) e11: revise’(e11,j,p1) j: John(j) ==> person(j) p1: paper(p1) Poss(x1,p1) x1: he(x1), Coref(x1,...,j,...)
e21: revise’(e21,t,p2) t: teacher(t) ==> person(t) p2: paper(p2) Poss(x2,p2) x2: Coref(x2,...,x1,...) he(x2), Coref(x2,...,t,...)
Strict: JJ
Sloppy: JT
Similarity or Semantic ParallelismBlood probably contains the highest concentration of hepatitis B virus of any tissue except liver.Semen, vaginal secretions, and menstrual blood contain the agent and are infective.Saliva has lower concentrations than blood, and even hepatitis B surface antigen may be detectable in no more than half of infected individuals.Urine contains low concentrations at any given time.
BODY MATERIAL CONTAINS CONCENTRATION AGENT
blood contains highest concentration HBV
semenvaginal secretions contain agentmenstrual blood
saliva has lower concentrations
(saliva of) infected in detectable ... no more HBsAgindividuals more than half
urine contains low concentrations
Elaboration
Elaboration(e1,e2,e) --> CoherenceRel(e1,e2,e) gen(e1,e) & gen(e2,e) --> Elaboration(e1,e2,e)
Go down First Street.Just follow First Street three blocks to A Street.
go(Agent: you, Goal: x, Path: First St., Measure: y) go(Agent: you, Goal: A St., Path: First St., Measure: 3 blks)
ElaborationSegment("Go .. A Street.", f)
CoherenceRel(g,f,f)
Segment("Go down 1st St.", g) Segment("Follow ... A St.", f)
Elaboration(g,f,f)
Syn("Go down 1st St.", g,-,-) Syn("Follow ... A St.", f,-,-)
gen(g,f) gen(f,f)follow'(f,u,FS,AS)
go'(g,u,x,y) along(g,FS)
down(g,FS)
Contrast
p'(e1,x) & not'(e2,e3) & p'(e3,y) & q(x) & q(y) --> CoRel(e1,e2,e2)
x and y are similar by virtue of property q. S1 and S2 assert contrasting properties p and ~p of x and y (e1 and e2). Second segment is dominant.
Mary is graceful. John is an elephant.
Mary is graceful. John is an elephant.
rel(e3,e2)
CoRel(e1,e2) Syn("John is an elephant",e2,-,-)
graceful'(e1,m)
Mary(m)
Syn(" is an elephant",e2,j,-)Syn("John",j,-,-)
Syn("an elephant",e2,j,-)
Syn(" is",e2,j,-)
Syn("an elephant",e3,j,-)
not'(e2,e4) & graceful'(e4,j)
Contrast(e1,e2)
John(j)
elephant'(e3,j) --> clumsy'(e2,j) & imply(e3,e2)
Present(e2)person(m) person(j)
Metaphor via Contrast Sentence's claim is
John's clumsiness
Coercionprotects fromcontradiction
This belief issource ofmetaphor
Search for coherence forcesmetaphor reading
AQUAINT-I: Question-Answeringfrom Multiple Sources
Show me the region 100 km north of the capital of Afghanistan.
What is the capitalof Afghanistan?
What is the lat/long100 km north?
What is the lat/longof Kabul?
CIAFact Book Geographical
Formula
QuestionDecomposition
via Logical Rules
AlexandrianDigital Library
Gazetteer
Show thatlat/long
Terravision
ResourcesAttached toReasoning
Process
A Complex QueryWhat recent purchases of suspicious equipment has XYZ Corp or its subsidiaries or parent firm made in foreign countries?
subsidiary(x,y)
parent(y,x)
Subsidiaries:XYZ: ABC, ...DEF: ..., XYZ, ...
illegal
biowarfare
DB of bio-equip
Ask User not USA
Purchase: Agent: XYZ, ABC, DEF, ... Patient: anthrax, ... Date: since Jun05 Location: --
Prove Question from AnswerQ: “How did Adolf Hitler die?”QLF: manner(e4) & Adolf(x10) & Hitler(x11) & nn(x12,x10,11) & die’(e4,x12)
ALF: it(x14) & be’(e1,x14,x2) & Zhukov(x1) & ’s(x2,x1) & soldier(x2) & plant’(e2,x2,x3) & Soviet(x3) & flag(x3) & atop(e2,x4) & Reichstag(x4) & on(e2,x8) & May(x5) & 1(x6) & 1945(x7) & nn(x8,x5,x6,x7) & day(x9) & Adolf(x10) & Hitler(x11) & nn(x12,x10,x11) & commit’(e3,x12,e5) & suicide’(e5,x12)A: “It was Zhukov’s soldiers who planted a Soviet flag atop the Reichstag on May 1, 1945, a day after Adolf Hitler committed suicide.”
“suicide” is troponym of “kill”: suicide’(e5,x12) --> kill’(e5,x12,x12) & manner(e5)
Gloss of “kill”: kill’(e5,x12,x12) <--> cause’(e5,x12,e4) & die’(e4,x12)
Gloss of “suicide”: suicide’(e5,x12) <--> kill’(e5,x12,x12)
e4=e5?
The Search Space Problem
120,000 glosses --> 120,000 axiomsTheorem proving would take forever.
Lexical chains / marker passing: Try to find paths between Answer Logical Form and Question Logical Form. Ignore the arguments; look for links between predicates in XWN; it becomes a graph traversal problem (e.g., confuse “buy”, “sell”) Observation: All proofs use chains of inference no longer than 4 steps Carry out this marker passing only 4 levels out
Q: “What Spanish explorer discovered the Mississippi River?”Candidate A: “Spanish explorer Hernando de Soto reached the Mississippi River in 1536.”Lexical chain: discover-v#7 --GLOSS--> reach-v#1
Set of support strategy: Use only axioms that are on one of these paths. 120,000 axioms ==> several hundred axioms
Relaxation (Assumptions)
Rarely or never can the entire Question Logical Form be proved from the Answer Logical Form ==> We have to relax the Question Logical Form
“Do tall men succeed?”
Logical Form: tall’(e1,x1) & x1=x2 & man’(e2,x2) & x2=x3 & succeed’(e3,x3)
Remove these conjuncts from what has to be proved, one by one, in some order, and try to prove again.
E.g., we might find a mention of something tall and a statement that men succeed.One limiting case: We find a mention of success.
Penalize proof for every relaxation, and pick the best proof.
Abduction
Observable: QGeneral principle: P --> Q
Conclusion, assumption, or explanation: P
Inference to thebest explanation
In the LCC QA system: The question is the observable: Hitler died The XWN glosses and troponyms are suicide --> kill --> die the general principles: The answer is the explanation: Hitler committed suicide
Relaxation is the assumptions you have to make to get the proof to go through.
Abduction: Try to prove Q the best you can; Make assumptions where you have to.
Coherence Relationsbetween Embeddings
The model shows that the human immune system is only able to mount an effective response against HIV quasispecies whose diversity is below some threshold value;
once the population of viral strains exceeds this "diversity threshold" the immune system is no long able to regulate viral replication.
The model shows that
^
Coherence Relations between Coercions
John must be at home.
His car is in the driveway.
I believe
I see that
^
^
CAUSE
Do We Rally RecognizeCoherence Relations?
Recognizing coherence relation = recognizing sentences as part of one discourse
"We don't recognize coherence relations. We just find the best interpretation of the whole text."
"We don't parse sentences. We just figure out the predicate-argument relations."
Outline
Interpreting Adjacency
What Coherence Relations are there?
Definitions and Examples of Specific Coherence Relations
Discourse Structure
Tree Structure from Multiple Adjacencies
[Cancer Research] Institute
vs. Stanford [Research Institute]
John [believes [men work]]
The Principal Informationin a Composite Segment
turpentine jar ==> jar
Stanford Research Institute ==> Institute
men work ==> work
John believes men work ==> believes
For full clause, the principal information is the assertion: main verb | top-level adverbials | high stress | new information | ....
The entity or eventuality that participates in higher-level structures.
Discourse Structure from
Multiple AdjacenciesHe was in a foul humor.He hadn't slept well.His electric blanket hadn't worked.
John got straight A's.He got a 1500 on his SATs.He is very intelligent.
The Principal Informationin a Discourse Segment
1. John got straight A's.2. He got a 1500 on his SATs.3. He is very intelligent.
To relate 1-2 to 3, we need a characterization of the principal information conveyed by 1-2.
Need to compute an Assertion or Summary for composite segments of discourse.
That’s the eventuality that participates in higher-level structures.
Tasks of a Discourse Theory
1. What are the possible relations between adjacent discourse segments?
2. How are they recognized or characterized?
3. What are the assertions / summaries of the composite segments?
Formalizing the Tree Structureof Discourse
Syn(s,e) --> Segment(s,e)
Segment(s1,e1) & Segment(s2,e2) & CoRel(e1,e2,e) --> Segment(s1 s2, e)
Note: Syntactic composition rules are an instance of this rule, where relation is pred-arg.
To interpret text, prove: ( e) Segment(text, e)
Summary
Hypotactic and Paratactic
Coherence RelationsHypotactic:
CoRel(e1,e2,e1)
Paratactic:
CoRel(e1,e2,e) where e is derived somehow from e1 and e2
Dominant Subordinate
Tree Building
Coherence Structure: Static, after-the-factFlow Model: Dynamic, play-by-play
NOT A REAL DISTINCTIONThe Tree-Building Operation:
N1 R(N1,N2) ==> R
N2N1
Discourse Pivots
A: Let's see. An hour for Brian, an hour and fifteen minutes for me, thirty five minutes for Charles. That's almost exactly three hours.
B: But they're typically late on these things.
SUMMATION
DISAGREEMENT(Elaboration)
(Contrast)
Discourse Pivot: In S1 S2 S3, S1 and S2 are related to each other by virtue of one part of the content of S2, and S2 and S3 are related to each other by virtue of another part of the content of S2.
Discourse CoherenceRelation
Segment1 Segment2
Interpret each segment, and find the relation between them.
causefigure-ground and ground-figuresimilarity and contrast
R4
R3
R1 R2
S1 S2 S3 S4 S5
The Structure of Discourse
Method for Analyzing Discourse
1. Find the major one or two breaks in the text, recursively, until single clauses.
2. Label the nonterminal nodes in the resulting tree with the coherence relations.
3. Make precise the knowledge that was used to justify this labelling.
4. Validate the hypothesized knowledge of Step 3 by finding other examples of the use of the same knowledge elsewhere in the corpus.
F(K,T) = I
Paragraph from Novel1. The town itself is dreary;
2. not much is there except the cotton mill, the two-room houses where the workers live, a few peach trees, a church with two colored windows, and a miserable main street only a few hundred yards long.
3. On Saturdays the tenants from the near-by farms come in for a day of talk and trade.
4. Otherwise the town is lonesome, sad,
5. and like a place that is far off and estranged from all other places in the world.
6. The nearest train stop is Society City,
7. and the Greyhound and White Bus Lines use the Forks Falls Road which is three miles away.
8. The winters here are short and raw,
9. the summers white with glare and fiery hot.--- Carson McCullers, The Ballad of the Sad Cafe, p. 1
Analysis of Paragraph from Novel
1 2 3 4 5 6 7 8 9
Contrast, Parallel
ParallelParallel
Contrast
Exemplification
Parallel
Elaboration
Fragment of Conversation
1. A: So, um, if I went first, let's say with for um, see I, 2. as I said, I need about an hour and fifteen minutes3. I could do the, my reporting on the ongoing project, ah, for that first hour.4. See if we total up all the time we need,5. let's see an hour for Brian,6. A: an hour and fifteen minute for me, B: So it's7. A: thirty five minutes B: almost exactly ...8. A: it's almost exactly correct. Three hours.9. B: But we've got to take into account that they're typically late on these things.10. B: All right, so we're gonna get squeezed someplace. A: Okay, right, right, okay. Um,11. A: I think what I'd be willing to do is if we get squeezed on the, uh if I go first and if we get squeezed I'll I'll eat the ah the time that we lose.
Analysis of Fragment of Conversation
1 2 3 4 5 6 7 8 9 10 11
Parallel
Elaboration
Elaboration
Elaboration
Elaboration
Parallel
Contrast
Contrast:Problem-Solution
Cause
Paragraph from Scientific TextWe propose that the genetic variability of HIV is not so much of acomplication, as the key to understanding the development of AIDS.
(R1) In particular, we examine a mathematical model for viral multiplication
that explicitly describes the interplay between the total diversityof viral strains
(which in general will increase over time)
and the suppressing capacity of the immune system.
(R2) The model shows that the human immune system is only able tomount an effective response against HIV quasispecies
whose diversity is below some threshold value;
(R3) once the population of viral strains exceeds this "diversity threshold"
the immune system is no longer able to regulate viral replication,
with consequent destruction of CD + cells.
Structure of Scientific TextR1: in particular
propose
not so much ... as ...
complication key
R2
examine
that
model describes
interplay
and
which
diversity increase
suppressing
shows
R3
able once
whose exceeds with
quasispecies below
no longer able
destruction
Analysis of Scientific TextR1: in particular
propose
not so much ... as ... (CONTRAST)
complication key
R2 (ELAB)
examine
that (ELAB)
model describes
interplay
and (CAUSE)
which
diversity increase
suppressing
shows
R3 (CONTRAST)
able once (CAUSE)
whose exceeds with(CAUSE)
quasispecies below
no longer able
destruction
(ELAB)
Summary
Discourse structure arises from the use and interpretation of adjacency.
Recognition of discourse structure is naturally embedded in the abduction framework.
A small number of coherence relations probably suffice, in combination with general interpretive mechanisms.
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