The distribution of discourse relations within and across turns in spontaneous conversation
Year: 2023

Abstract:
Time pressure and topic negotiation may impose constraints on how people leverage discourse relations (DRs) in spontaneous conversational contexts. In this work, we adapt a system of DRs for written language to spontaneous dialogue using crowdsourced annotations from novice annotators. We then test whether discourse relations are used differently across several types of multi-utterance contexts. We compare the patterns of DR annotation within and across speakers and within and across turns. Ultimately, we find that different discourse contexts produce distinct distributions of discourse relations, with single-turn annotations creating the most uncertainty for annotators. Additionally, we find that the discourse relation annotations are of sufficient quality to predict from embeddings of discourse units.

heading: 1 Introduction
text: Discourse relations (DRs) such as Elaboration, Background and Explanation, hold between discourse units contributing to the coherence of a text. Annotation of discourse relations has received attention for its relevance to discourse parsers, with applications in question answering systems (e.g. Jansen et al., 2014), text summarization (e.g. Liu and Chen, 2019), sentiment classification (e.g. Kraus and Feuerriegel, 2019), and machine translation (e.g. Meyer and Popescu-Belis, 2012). However, most of the annotated data and systems have focused on written language, with a few exceptions (e.g., Tonelli et al., 2010; Zeldes, 2017; Scholman et al., 2022). In spoken dialogue or multiparty conversation, participants must quickly juggle a variety of tasks, such as responding to another person to solve a problem (Levinson and Torreira, 2015) or negotiating the question under discussion (Roberts, 2012), often under considerable time pressure that is less present in written production. In addition to these time demands, it is unclear whether spon-
taneous conversation demonstrates the same patterns of discourse relations as observed in written language (see Crible and Cuenca, 2017, for a discussion of spoken vs. written use of discourse markers).
Perhaps unsurprisingly, the vast majority of work on discourse relations has focused either on written texts, especially news text (Carlson et al., 2003; Prasad et al., 2008, 2018), or highly structured conversations that are constrained by a particular game (Afantenos et al., 2015; Asher et al., 2016). Some recent corpora contain spoken monologues (Scholman et al., 2022), and spoken conversations (Tonelli et al., 2010; Zeldes, 2017), but the field still largely lacks annotated corpora of spontaneous dialogue.
Thus, our goal is to present the first efforts towards an annotated corpus of DRs for spontaneous spoken conversation, with particular attention to relations across different contexts within a conversation. We analyze the patterns of DR annotation within and across speakers and within and across turns and test the coherence of annotators’ decisions.

heading: 2 Related Work
text: Most currently available corpora annotated with DRs have focused on written language or spoken monologues. An exception is the Georgetown University Multilayer (GUM) corpus (Zeldes, 2017), which has a set of conversations annotated within Rhetorical Structure Theory (RST, Mann and Thompson, 1987), following the guidelines of the RST Discourse Treebank (RST-DT, Carlson et al., 2003). But it is an open question whether the DRs that have been identified for news texts are appropriate for conversational data. Tonelli et al. (2010) adapt the PDTB framework to annotate a subset of a corpus of Italian conversations about software and hardware troubleshooting, and suggest modifications to the framework to account for
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spoken data.
Discourse relations corpora have usually been annotated by experts, but some recent corpora have been annotated by novice annotators, such as university students, in the case of the GUM corpus (Zeldes, 2017), or crowdsourced workers, in the case of the DiscoGEM corpus (Scholman et al., 2022). GUM was annotated using RST as part of a Corpus Linguistics class, while DiscoGEM was annotated following the Penn Discourse Treebank (PDTB, Prasad et al., 2008, 2018) framework, using a method for crowdsourcing annotations introduced in Yung et al. (2019), and using a multi-label approach. The present work deviates from prior work in its focus on conversational data and the use of Segmented Discourse Representation Theory (SDRT, Asher and Lascarides, 2003) alongside the STAC corpus (Asher et al., 2016) guidelines.

heading: 3 Discourse relation annotation
text: In this work, we focus on a subset of 19 dialogues from the Switchboard Corpus (Godfrey et al., 1992). This corpus contains informal language and has been the subject of study of numerous analyses of dialogue within linguistics (Jaeger and Snider, 2013; Reitter and Moore, 2014). In it, two strangers are presented with a topic (e.g., childcare) that they must discuss with each other, but the dialogues are otherwise not tightly constrained. Annotating Switchboard will provide us with a more complete understanding of the use and generality of discourse relations across linguistic contexts and genres.
Following the annotation procedure in the STAC corpus (Asher et al., 2016), we identified a subset of suitable elementary discourse units (EDUs) for annotation by parsing each turn into a dependency structure and included only those turns with at least two roots or verbs. Then, we segmented each of these turns into their respective EDUs. Using these segmentations, we identified EDU candidates for discourse relations that were either within-turn (same speaker) or across two turns (different speakers, or the same speaker), where the two turns were adjacent in the case of different speakers, or only interrupted by one turn, in the case of same speaker. We provide a representative set of these pair types in Table 1 under the Explanation, Comment, and Result examples, respectively.

heading: 3.1 Elementary Discourse Units
text: Elementary discourse units (EDUs) are typically defined as non-overlapping text spans (Mann and Thompson, 1987), which perform some basic discourse function (Asher and Lascarides, 2003), typically at the level of clauses. However, conversational EDUs may not necessarily contain a main verb (e.g., clarification questions: “Saginaw?”) or may be incomplete or interrupted (e.g., “and so–”). So, we define EDUs in Switchboard similarly to written text, with some modifications to account for variability due to spoken language. In particular, our modifications account for noise; non-linguistic communication (e.g., laughter); restarts; and disfluencies (e.g., “uh” or “um”). Additionally, we use complex discourse units (CDUs), which are combinations of EDUs which function together as an argument to a DR (Asher and Lascarides, 2003).

heading: 3.2 Relation categories
text: Discourse relations (DRs) were selected from Segmented Discourse Representation theory (SDRT, Asher and Lascarides, 2003), following the annotation manual for the STAC corpus (Asher et al., 2012). 11 out of 16 relation labels used in Asher et al. (2012) were selected, based on a pilot annotation. We selected the most common relations in an attempt to minimize the number of choices presented to annotators, but the set is non-exhaustive. An "Other" category was added for cases in which none of the selected labels applied. Table 1 shows the list of DRs together with representative examples.

heading: 3.3 Annotators
text: The present study recruited 114 students enrolled in a computational linguistics course grouped into 19 teams consisting of approximately 5 members who annotated the dyads. Each team received a conversation for annotation. Annotations were performed individually, but groups then discussed their work and submitted a report as a team. One team was excluded because they completed their annotations together and submitted a single set of labels. Students were trained to identify discourse relations using a short quiz and live training with the instructor of the course. Annotators were provided with guidelines to which they could refer back, and they had read and annotated the conversation in three previous tasks before annotating discourse relations, to ensure that they were familiar with the
topics and speakers in each dyad.

heading: 3.4 Annotation procedure
text: Annotators were presented with pairs representing either an EDU or CDU (π1) and another EDU or CDU (π2). Annotators were shown two spans of text π1 and π2 with π1 presented in italics and π2 presented in bold face font in the annotation software Prodigy (Montani and Honnibal, 2018), with two preceding and two subsequent turns for context. Annotators were asked to determine the relation between π1 and π2 from a list of the DR categories in Table 1. If annotators thought that no relation was present, they were told to reject the item and move on to the next pair. Critically for our research question, annotators could mark several relations for a pair of EDUs simultaneously. In addition to labeling discourse relations, annotators were also asked to provide a confidence rating on a scale from 1-5, but we leave these analyses for future work. In total, each annotator provided judgments for an average of 25 EDU pairs across 464 total pairs.
In the next section, we test whether annotators show greater uncertainty about discourse relations in different discourse contexts. We analyze the distribution of their labels to assess whether discourse relations in conversation vary in their contexts of use.

heading: 4 Uncertainty in the annotation of discourse relations
text: Different EDU pairs in the present annotation task were drawn either from the same turn, or across turns but within or across speakers. Thus, we can assess how much discourse relations vary by the placement of an utterance in a dialogue. Given the complex dynamics in dialogue, we expect to find significant differences in discourse relation use across different discourse contexts. We visualize the distribution of the relations in Figure 1.
Annotators generally selected more discourse relations per EDU pair in the single-turn case, with an average of 8.16 relations per team or 1.60 per annotator. When EDUs spanned turns within a single speaker, groups selected significantly fewer relations (average = 7.29, t(302) = −2.16, p < .05). Groups likewise selected even fewer relations for EDUs between two speakers (average = 6.51, t(314) = −2.54, p < .05). On its face, this pattern appears surprising, because it suggests that annotators find more relations appropriate for single-speaker productions. However, an alternative interpretation of these results is that annotators may instead have been uncertain about the distinctions between the different discourse relations. This second interpretation is corroborated by post-hoc poll data from 35 annotators, of whom 32 (91.4%) stated that the selection of discourse relations was best suited to annotating cross-speaker
EDU pairs. Future work will require recruiting greater numbers of annotators to be able to distinguish between these two hypotheses.

heading: 4.1 Inter-annotator agreement
text: We computed measures of inter-annotator agreement for multilabel tasks using Marchal et al. (2022). This approach uses bootstrap sampling to estimate the chance frequencies of DRs in a multilabel dataset to provide a baseline for agreement between annotators.
We summarize the results of this analysis in Table 2. Following Marchal et al. (2022), we computed observed, expected and adjusted agreement for six measures. Soft-match agreement uses the intersection of labels selected by two annotators; boot-match corrects the expected agreement by using the bootstrapping method (as opposed to ignoring non-intersecting labels); augmented kappa uses DR labels weighted according to the number of labels annotated for each item; precision and recall are calculated as the proportion of intersecting DR labels over the set of labels selected by the first and second annotator, respectively; F1 is the usual harmonic mean between precision and recall.
Both observed and adjusted agreement metrics were well above chance using the bootstrapping method proposed by Marchal et al. (2022). Agreement is in general modest (Landis and Koch, 1977), which may be partly due to the challenging nature of the DRs annotation task (Spooren and Degand,
2010), and partly due to annotators’ uncertainty on DR labels across different context types.

heading: 4.2 Predicting relation selection
text: We use a model comparison approach to understand the contributions of discourse context (within/across speakers and within/across turns) to relation annotation by first constructing a null model that estimates the base rates of each dis-
course relation. Then, we constructed a multiclass logistic regression model containing the discourse context variables of interest, which significantly improved fit to the annotation data (X2(22) = 447.98, p < .001). This improvement in fit suggests that the distribution of discourse relations that are identified by annotators is distinct across contexts. Adding the annotator group/topic also significantly improved fit beyond the model containing the contextual variables alone (X2(198) = 900.06 p < .001). We summarize the results of this final model in Table 3.1
An informal evaluation of the coefficients suggests that discourse relations are not uniformly distributed across contexts. Intuitively, Acknowledgements, Clarification Questions, Comments, and Question-Answer Pairs are more likely across speakers than within. Additionally, Continuations, Elaborations, Explanations, and Narrations are more likely to occur within a single speaker. The pattern of results is more unclear when comparing EDUs that are produced by a single speaker but which occur either within or across turns. For example, relations such as Clarification Questions are less likely to occur within a turn than across turns.

heading: 4.3 Classifier for relations
text: To validate the quality of the annotations, we built a model to classify EDU pairs into discourse relations. We reasoned that if annotators are following the guidelines and use information about the EDU pairs, then a classifier should be able to predict DR labels. We encoded the first EDU or CDU (π1) and the second (π2) as the two “sentences” in the next sentence prediction architecture of BERT (Devlin et al., 2019). This enables the classifier to represent the π1 and π2 components somewhat separately.
We built a classifier head trained on the resulting embeddings without fine-tuning to predict each individual annotator label. We chose to model each annotator label individually to learn agreement/majority class implicitly because prior studies have shown that this improves generalization (Yung et al., 2022). We use a leave-one-conversation-out training procedure, in which we test a ridge regression classifier on all of the annotations from a single conversation while we train it on all other
1Due to the multilabel nature of the annotation task and the one-versus-rest training for the multiclass model, coefficients for each DR are not independent, were not estimated jointly, and should be interpreted broadly as representing separate logistic regressions.
annotations across the other conversations. This ensures minimal memorization of specific turns within a conversation, which is critical given our multilabel annotation approach.
Strict annotation-level accuracy to predict each selected label from all annotators was quite poor, with macro average precision at .21, recall at .19, and F1 at .19. However, recall was substantially higher when considering whether the top guess belonged to the set of all labels provided by annotators, at .76 overall and .71 averaged by group.
To quantify the uncertainty of the annotators across different contexts, we leverage the classifier to produce a label distribution for a given (π1, π2) pair. We then compute the cross-entropy between the model’s predictions and annotators’ gold label distributions, collapsing across all annotations for an EDU pair. Overall, cross entropy between model predictions and annotator labels was highest for the single-turn case, with (mean = 0.43), but lowest for EDUs between two speakers (mean = 0.38), suggesting greater uncertainty in label assignment.

heading: 5 Discussion
text: In two experiments, we demonstrated that novice DR label annotations in a single turn are more difficult than across turns. We showed that including discourse context (within/across speaker and within/across turn) to a logistic regression model significantly improves fit to our annotation data. A classifier trained to predict DR labels from embeddings of (π1, π2) pairs showed modest success for recall of any of the annotations, but poor precision and recall overall. A comparison of this classifier’s predictions and annotators’ gold label distributions revealed greater uncertainty for the annotation of discourse relations within a single turn.
These results demonstrate that different conversational contexts are associated with different distributions of discourse relations. The uncertainty of choice of discourse relations within a turn may be due to several factors. DRs that typically occur across adjacent turns and across speakers (e.g., Acknowledgements) might have clearer signals. At the same time, DRs that occur more frequently within speakers, and, in particular, within a turn, might be more ambiguous, or might co-occur with other relations. More work is necessary to disentangle uncertainty about the identity of the best fit relation from whether multiple relations are appropriate.
Limitations
The current work is limited by the size of the dataset and the nature of spontaneous conversation. While the discourse relations proposed as part of this work were selected to be general and build on categories from the literature, the list is not exhaustive and it is likely that these relations may be culturally, linguistically, and situationally specific. Future work in this area should validate the generality of the discourse relation system used in this work.
The selection of EDUs and CDUs for annotation is also non-exhaustive; additional segments could be included in future work.
Annotation quality is also a practical limitation. Annotation for discourse relations typically results in low-agreement data, even among expert annotators (e.g., DiscoGEM; Scholman et al., 2022). Even though our research questions focus on this disagreement as a positive, other researchers may require greater numbers of annotations in order to obtain a gold label.
Ethics Statement
We are not aware of ethical issues associated with the texts used in this work. Students participated in the annotation task as part of course credit but annotation decisions were not associated with their performance in the course.

heading: Acknowledgements
text: We would like to thank Jürgen Bohnemeyer and three anonymous reviewers for feedback on a previous version of this paper.

heading: Future Work
text: Future work in this area should validate the generality of the discourse relation system used in this work. The selection of EDUs and CDUs for annotation is also non-exhaustive; additional segments could be included in future work. Annotation quality is also a practical limitation. Annotation for discourse relations typically results in low-agreement data, even among expert annotators (e. g. , DiscoGEM; Scholman et al. , 2022). Even though our research questions focus on this disagreement as a positive, other researchers may require greater numbers of annotations in order to obtain a gold label