Lecture 23: Rhetorical Structure Theory

So far we've seen flat representations of discourse.  RST is an example of a hierarchical discourse representation.  Such as:

Here, we've broken some imagined text into 7 "units" and depicted the role of these units in the text.  As is implied by the title, this theory is mostly applicable to rhetoric, which is essentially persuasive language.

An RST structure is essentially a dependency tree over elementary discourse units (EDUs), where relations on the edges of the tree tell us the relationship between two EDUs.  In the above example, we're saying that EDUs 1-3 provide background to EDUs 4-7.  And so on down the tree.

For most relations, there is a distinction between the nucleus and satellite of that relation: basically this is just like headedness in syntax.  The nucleus contains the important stuff.

The relations in RST are given by communicative intent.  This is a big separation between RST and other theories of discourse.

Here is an example of the evidence relation:

• The program as published for the calendar year 1980 really works.
• In only a few minutes, I entered all the figures from my 1980 tax return and got a result which agreed with my hand calculations to the penny.

Here, the first sentence is the nucleus and the second is the satellite.  The evidence schema states (R=reader, W=writer, N=nucleus, S=satellite):

• constraints on N: R might not believe N to a degree satisfactory to W
• constraints on S: R believes S or will find it credible
• constrains on N+S: R's comprehending S increases R's belief of N
• the effect: R's belief of N is increased
• locus of effect: N

 Here's an example for concession:

• Concern that this material is harmful to health or the environment may be misplaced.
• Although it is toxic to certain animals,
• evidence is lacking that it has any serious long-term effect on human beings.

Here, the 2nd EDU is a concession to the 3rd EDU (note that 2 and 3 are both in the same sentence).  The concession schema looks like:

• constraints on N: W has positive regard for the situation presented in N
• constraints on S: W is not claiming that the situation presented in S doesn't hold
• constraints on N+S: W acknowledges a potential or apparent incompatibility between the situations presented in N and S; W regards the situations presented in N and S as compatible; recognizing that the compatibility between the situations presented in S and S increases R's positive regard for the situation presented in N
• the effect: R's positive regard for the situation presented in N is increased
• locus of effect: N and S

Here is the original list of relations from the Mann+Thompson paper, though others have been added over time:

• Circumstance
• Solutionhood
• Elaboration
• Background
• Enablement and Motivation
• Evidence and Justify
• Relations of Cause
• Antithesis and Concession
• Condition and Otherwise
• Interpretation and Evaluation
• Restatement and Summary
• Sequence
• Contrast

Spotting discourse effects is quite hard, which I have previously bemoaned.  In general, there are two tasks: splitting sentences into EDUs and then doing discourse parsing.  Unfortunately, the main foothold we have for both of these tasks are lexical cues.  (For EDU splitting, embedded S-BARs often, though don't always, indicate a new EDU.)

For instance, concession is often identified by the word "although."  And "Evidence" is often identified by "For instance."  And "Elaboration" is often identified by "And."  And so on.

One clever idea a few years ago was to try to mine lexical relations that are indicative of discourse structure.  For example, I can find all sentences that begin "for example" and look at that sentence, and the preceding sentence.  I can assume that this is an example of Evidence, and then look at features of those two sentences to try to figure out why this is an evidence relation.  Then, in the future, when I see sentences that don't have this lexical cue, I can apply whatever I've learned.

The hope is that if you mine contrast relations, you can find contrasting pairs like love/hate or iPhone/Android or whatever.  As was shown in the assigned paper for today, that didn't work particularly well.  My feeling is that lexical information is not "deep" enough to really get you to discourse except in very simple cases (see that post I linked to before).