Encoding Discourse Structure: Comparison of RST and QUD (2023)

Abstract: We present a quantitative and qualitative comparison of the discourse trees defined by the Rhetorical Structure Theory and Questions under Discussion models. Based on an empirical analysis of parallel annotations for 28 texts (blog posts and podcast transcripts), we conclude that both discourse frameworks capture similar structural information. The qualitative analysis shows that while complex discourse units often match between analyses, QUD structures do not indicate the centrality of segments.

## introduction rhetorical structure theory (rst) @xcite and the question under discussion (qud) model (e.g., @xcite @xcite are two accounts of discourse structure that stem from different research fields and aim to explain different phenomena (speaker intentions and rhetoric versus information structure). however, they share a fundamental formal assumption: that discourse structure is to be represented as a tree that is constructed by recursively combining adjacent "elementary units" of the discourse. for qud, "discourse" originally meant primarily dialog, while rst was designed for monologue text. nonetheless, researchers have occasionally explored ways to apply one theory also to the mode of the other. in this paper, we systematically compare these two approaches to discourse structure, based on empirical observations in a novel multi-media corpus. while some researchers have previously noted the intuitive similarities of rst based trees and qud based trees on a theoretical level, this work presents the first study where both frameworks are systematically applied to a corpus of both spoken and written data, and compared in a quantitative and qualitative manner. ## related work in annotating both media present in our corpus, we apply both discourse models outside of what they have been primarily designed for. while qud has been applied to monologue texts before @xcite , it is most centrally applied to (short) dialogs. on the other hand, rst aims to capture the intentional structure constructed by the writer and is thus designed for monologue, but it has been occasionally applied to dialog, as well (e.g., @xcite . the qud framework has been developed to capture aspects of the information structure of sen-tences and certain specific pragmatic phenomena @xcite . only recently has it been used for annotating larger texts, and compared to models of the coherence structure of discourse. @xcite compare qud structures to those proposed by segmented discourse representation theory (sdrt). they argue that although there likely is no qud corresponding to every discourse relation, "quds correspond to complex discourse units in a discourse graph" (p. 41), that is, topics that lead to grouping discourse units together. @xcite similarly starts by comparing qud and sdrt and argues that formally analyzing the erotetic (i.e., question) structure of a discourse can be useful to understand its meaning and its relation structure, for example its sdrt representation. he develops a method for mapping (parts of) question graphs to sdrt representations, and takes a close look at the result relation as a case study. he argues that qud theories (in particular, models based on potential questions) have repercussions for the larger discourse structure of a text (as represented for example in sdrt). @xcite offer the first detailed guidelines for segmenting discourse and annotating qud trees in authentic text, discussing individual texts from english, french, and german and from three different genres. one claim is that the same guidelines apply to monologue (newspaper articles) and dialog (interviews) alike. in later work, @xcite compare qud, rst, and the ccr approach to discourse structure for one text. regarding segmentation rules in qud and rst, they point out that qud calls for smaller segments than rst when information-structural factors suggest a discourse contribution, e.g., for contrastive foci. conversely, adjunct clauses can sometimes be separate segments in rst (e.g., in circumstance relations) but not in qud. as for the relations, the authors show how rst relations can be integrated into a qud tree notation, and discuss some typical mappings. @xcite presents a proposal to include both sub-and coordinating relations in a tree that combines qud and sdrt, based on the approach by @xcite . for example, the temporal progression of a discourse can be represented by questions asking about each point in time: what happened at t 1:n ? finally, we mention that an early (but somewhat inconclusive) debate in the rst community on the interplay of "intentional" and "informational" coherence relations (e.g., @xcite )) foreshadowed the kind of duality that rst and qud embody. ## data and method we carry out an empirical comparison of qudand rst-based annotations of the same texts in two media. the idea is based on the assumption that while the qud and rst frameworks cannot be directly mapped onto one another, both aim to capture the overall coherence of a discourse in a tree-like fashion. thus, previous work such as @xcite has proposed to study the correspondences in these discourse trees by looking at the relation between rhetorical relations and question-based structures. our study is the first, to our knowledge, which carries out a parallel annotation of both spoken and written texts in the two frameworks. our data and annotation process is described here. ## quantitative analysis of rst and qud correspondence we automatically evaluated the similarity of the rst and qud discourse structures quantitatively across the whole corpus. to do this, we computed a variant of the parseval measure known from evaluating (syntactic) constituency parse trees. this measure compares unlabelled trees a and b: first, for all nodes n a in tree a, one determines whether tree b contains a node with the same yield (= concatenation of all the text dominated by a node) as n a . in the case of comparing an automatic parse tree with a gold standard, this would reflect precision. second, for all nodes n b in b, the same is repeated (∼ recall). we finally compute the harmonic mean of both directions to determine the similarity of rst and qud structures. we use unlabelled parseval scores because intermediate nodes are labelled with relations in the case of rst trees, and with questions in qud trees. 6 note that the standard parseval score includes leaf nodes in its computation (in the case of syntactic trees, pos tags), and that this practice typically leads to much higher scores than excluding leaves. in our computation, we also include leaves, not only because it is commonly done, but also because in our tree structures, certain error cases can only be reflected when including leaf nodes. this happens in particular, when an explicit text segment is used as an explicit question under discussion (i.e., internal node) in a qud tree. for this reason, we also redefine the "yield" of a node to be all explicit text dominated by that node, both when it is represented in a leaf node and when it is in an intermediate node. the results of the quantitative comparison are shown in ## qualitative comparison of rst and qud trees to further evaluate the correspondences between the analyses, we take a closer look at our annotations. first, we inspected the five pairs of trees that received the lowest matching scores in the quantitative comparison; here we note that a frequent source of mismatch is rst's tendency to build a complex discourse unit in cases where qud attaches the material locally (see below). then we turned also to the other pairs, trying to generalize sources of misalignment. section 6.1 compares the way complex discourse units are constructed in both models.the overall structure of texts annotated with both models seems to often be similar, yet there are instances where the structures are quite different. sections 6.2 and 6.3 compare how translatable different rhetorical relations are to qud trees and whether rst trees are able to represent typical characteristics of dialog, like speaker changes. ## discussion in this paper, we have carried out a systematic comparison of the discourse structures induced by rst and qud frameworks for the same texts from two media, blog posts and podcast transcripts. our annotations of the 28 texts show that both frameworks can be successfully applied to monologue texts as well as dialogs. we compare the branching structure of the resulting rst and qud trees by first providing a method that converts an rst tree into an equivalent qud representation. we compare these representations to the manually annotated qud trees and find an overall similarity of 0.74 -similar to or surpassing the agreement scores between human annotators for discourse structure annotation tasks. this shows that the two frameworks cover some of the same information for our corpus. the similarity between analyses is higher for the blog posts, indicating that the topic structure (qud) and coherence/intentional structure (rst) are more closely related for monologue texts than for dialog (where two speakers have to agree on how the overall discourse progresses). finally, we provide a detailed qualitative comparison of the way complex discourse units are mapped across frameworks, about how certain discourse relations can be represented in qud trees, and the effects of speaker changes in dialog. we discuss that while the overall structure of qud and rst trees often matches approximately, qud trees do not indicate the centrality of discourse segments and cannot represent certain types of relations easily, such as concession and contrast. in contrast, the topic progression within a discourse is captured in qud analyses but may be missing from rst.